Merge pull request #1 from sinmb79/codex/birdseye-v2

[codex] add Gemini-powered birdseye rendering
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2026-04-04 19:33:18 +09:00
committed by GitHub
22 changed files with 2475 additions and 396 deletions

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# python setup_keys.py --from-env-file .env # python setup_keys.py --from-env-file .env
DATA_GO_KR_API_KEY= DATA_GO_KR_API_KEY=
VWORLD_API_KEY= VWORLD_API_KEY=
GEMINI_API_KEY=

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README.md
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# Construction-Project-Planning-Master-MCP # CivilPlan MCP v2.0.0
**건설/건축 공사 사업계획을 AI와 함께 만듭니다** 한국형 토목·건축 프로젝트 기획을 MCP 도구로 구조화하고 문서·도면·3D 렌더까지 생성하는 서버입니다.
**Plan Korean construction projects with AI assistance** CivilPlan MCP is an MCP server for Korean civil and building project planning that produces structured analysis, documents, drawings, and 3D renders.
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
[![Python 3.11+](https://img.shields.io/badge/Python-3.11+-green.svg)](https://python.org) [![Python 3.11+](https://img.shields.io/badge/Python-3.11+-green.svg)](https://python.org)
[![FastMCP](https://img.shields.io/badge/FastMCP-2.0+-orange.svg)](https://github.com/jlowin/fastmcp) [![FastMCP 2.0+](https://img.shields.io/badge/FastMCP-2.0+-orange.svg)](https://github.com/jlowin/fastmcp)
[![Version 2.0.0](https://img.shields.io/badge/version-2.0.0-black.svg)](pyproject.toml)
---
## 소개 | Introduction ## 소개 | Introduction
CivilPlan MCP는 한국 토목/건축 사업의 **기획 단계 전 과정**을 AI가 지원하는 MCP(Model Context Protocol) 서버입니다. Claude, ChatGPT 등 AI 에이전트가 이 서버의 도구를 호출하여 사업비 산출, 법적 절차 확인, 도면 생성 등을 자동으로 수행합니다. CivilPlan MCP는 자연어 프로젝트 설명을 받아 인허가 검토, 물량 산정, 단가 조회, 투자 문서 작성, SVG/DXF 도면 생성, 3D Bird's-Eye View 렌더링까지 연결하는 20개 MCP 도구를 제공합니다.
CivilPlan MCP provides 20 MCP tools that turn natural-language project requests into permit reviews, quantity takeoff, pricing, planning documents, SVG/DXF drawings, and 3D bird's-eye renders.
CivilPlan MCP is a FastMCP server that helps AI agents (Claude, ChatGPT, etc.) plan Korean civil engineering and building projects. It automates cost estimation, legal procedure identification, drawing generation, and more -- all through the MCP protocol. 주요 흐름은 `프로젝트 파싱 → 법적·사업성 검토 → 산출물 생성`입니다.
The core flow is `project parsing → legal/financial review → output generation`.
> **철학 | Philosophy**: 제4의길-AI와 함께 새로운 세상을 만들어갑니다. -- 전문 기획 지식에 대한 접근 불평등을 줄입니다. ```mermaid
> Reduce inequality in access to expert planning knowledge. Free to use, modify, and distribute. flowchart LR
![시스템 개요 System Overview](docs/images/01_system_overview.png) A["사용자 요청<br/>Natural-language request"] --> B["civilplan_parse_project"]
![워크플로우 Workflow](docs/images/02_workflow.png) B --> C["법적·기획 분석<br/>legal / planning analysis"]
![단계별 다이어그램 Phase Diagram](docs/images/03_phase_diagram.png) B --> D["물량·단가·사업성<br/>quantity / pricing / feasibility"]
![프로세스 흐름 Process Flow](docs/images/04_process_flow.png) B --> E["문서·도면 생성<br/>docs / drawings"]
![사업비 요약 Cost Summary](docs/images/05_cost_summary.png) B --> F["3D 렌더 생성<br/>bird's-eye / perspective render"]
![사업 일정 Project Timeline](docs/images/06_project_timeline.png) C --> G["MCP 응답 JSON"]
![종합 뷰 Comprehensive View](docs/images/07_comprehensive_view.png) D --> G
![출력 예시 Output Examples](docs/images/08_output_examples.png) E --> G
![문서 생성 Document Generation](docs/images/09_doc_generation.png) F --> G
![상세 워크플로우 Detailed Workflow](docs/images/10_detailed_workflow.png) ```
![평면도 Plan View](docs/images/11_plan_view.png)
---
## 이런 분들에게 유용합니다 | Who Is This For? ## 누가 쓰면 좋은가 | Who Is This For
| 대상 | 활용 예시 | | 대상 Audience | 쓰는 이유 Why |
|------|----------| |---|---|
| **지자체 공무원** | 도로/상하수도 사업 기획 시 개략 사업비와 인허가 절차를 빠르게 파악 | | 지자체·공공 발주 담당자<br/>Local government and public-sector planners | 초기 타당성, 절차, 예산 초안이 빠르게 필요할 때 사용합니다.<br/>Use it when you need a fast first-pass on feasibility, procedures, and budget. |
| **건설 엔지니어** | 기획 단계 물량/단가 산출, 투자계획서 초안 작성 자동화 | | 토목·건축 엔지니어<br/>Civil and building engineers | 기획 단계 물량, 단가, 문서 초안 자동화할 수 있습니다.<br/>Automate early-stage quantity takeoff, pricing, and planning documents. |
| **부동산 개발 기획자** | 개발 사업의 법적 절차, 영향평가 대상 여부 확인 | | 개발사업 기획자<br/>Development planners | 자연어 설명만으로 구조화된 프로젝트 데이터와 시각 자료를 얻을 수 있습니다.<br/>Turn a plain-language project brief into structured project data and visuals. |
| **건축주/시행사** | AI에게 자연어로 사업 설명 -> 구조화된 사업 계획 문서 일괄 생성 | | AI 에이전트 운영자<br/>AI agent builders | Claude, ChatGPT, 기타 MCP 클라이언트에 토목/건축 전용 도구 세트를 연결할 수 있습니다.<br/>Attach a Korean construction-planning toolset to Claude, ChatGPT, and other MCP clients. |
| **학생/연구자** | 한국 건설 법령/표준품셈 학습 및 시뮬레이션 |
| Who | Use Case |
|-----|----------|
| **Local government officials** | Quickly estimate project costs and permits for road/water projects |
| **Civil engineers** | Automate preliminary quantity takeoff, unit pricing, and investment reports |
| **Real estate developers** | Identify legal procedures and impact assessments for development projects |
| **Project owners** | Describe a project in natural language -> get structured planning documents |
| **Students & researchers** | Learn Korean construction law, standard specifications, and cost estimation |
---
## 주요 기능 | Key Features ## 주요 기능 | Key Features
### 19개 AI 도구 | 19 AI Tools ### v2.0.0 변경점 | What's New in v2.0.0
CivilPlan은 사업 기획의 전 과정을 커버하는 19개 도구를 제공합니다: | 항목 Item | 설명 Description |
|---|---|
| `civilplan_generate_birdseye_view` | Gemini 기반 3D Bird's-Eye View와 Perspective View를 한 번에 생성합니다.<br/>Generates Gemini-based bird's-eye and perspective renders in one call. |
| `GEMINI_API_KEY` 설정 | `.env`와 Windows DPAPI 기반 `setup_keys.py` 양쪽에서 Gemini 키를 읽습니다.<br/>Reads the Gemini key from both `.env` and Windows DPAPI-based `setup_keys.py`. |
| 도메인 프롬프트 템플릿 | 도로, 건축, 상하수도, 하천, 조경, 복합 프로젝트별 렌더 문구를 분리했습니다.<br/>Adds domain-specific render prompts for road, building, water, river, landscape, and mixed projects. |
| 총 20개 MCP 도구 | 기존 기획/문서/도면 도구에 3D 시각화를 추가했습니다.<br/>Expands the server to 20 MCP tools by adding 3D visualization. |
| # | 도구 Tool | 설명 Description | ### 도구 목록 | Tool Catalog
|---|-----------|-----------------|
| 1 | `civilplan_parse_project` | 자연어 사업 설명 -> 구조화된 사업 정보 추출 / Parse natural language project description |
| 2 | `civilplan_get_legal_procedures` | 사업 유형/규모별 법적 절차 자동 산출 / Identify applicable legal procedures |
| 3 | `civilplan_get_phase_checklist` | 사업 단계별 체크리스트 생성 / Generate phase-specific checklists |
| 4 | `civilplan_evaluate_impact_assessments` | 9종 영향평가 대상 여부 판단 / Evaluate 9 types of impact assessments |
| 5 | `civilplan_estimate_quantities` | 표준 횡단면 기반 개략 물량 산출 / Estimate quantities from standard cross-sections |
| 6 | `civilplan_get_unit_prices` | 공종별 단가 조회 (지역계수 반영) / Query unit prices with regional factors |
| 7 | `civilplan_generate_boq_excel` | 사업내역서(BOQ) Excel 생성 / Generate BOQ spreadsheet |
| 8 | `civilplan_generate_investment_doc` | 투자계획서(사업계획서) Word 생성 / Generate investment plan document |
| 9 | `civilplan_generate_schedule` | 사업 추진 일정표 (간트차트형) 생성 / Generate project schedule |
| 10 | `civilplan_generate_svg_drawing` | 개략 도면 SVG 생성 (평면도, 횡단면도) / Generate SVG drawings |
| 11 | `civilplan_get_applicable_guidelines` | 적용 기준/지침 조회 / Get applicable guidelines |
| 12 | `civilplan_fetch_guideline_summary` | 기준/지침 요약 조회 / Fetch guideline summaries |
| 13 | `civilplan_select_bid_type` | 발주 방식 선정 / Select bid type |
| 14 | `civilplan_estimate_waste_disposal` | 건설폐기물 처리비 산출 / Estimate waste disposal costs |
| 15 | `civilplan_query_land_info` | 토지 정보 조회 (PNU, 용도지역) / Query land info |
| 16 | `civilplan_analyze_feasibility` | 사업 타당성 분석 / Analyze project feasibility |
| 17 | `civilplan_validate_against_benchmark` | 유사 사업비 벤치마크 검증 / Validate against benchmarks |
| 18 | `civilplan_generate_budget_report` | 예산 보고서 생성 / Generate budget report |
| 19 | `civilplan_generate_dxf_drawing` | DXF 도면 생성 (CAD 호환) / Generate DXF drawings |
### 지원 사업 분야 | Supported Project Domains #### 기획·분석 도구 | Planning and Analysis Tools
- `건축` -- 건축물 (Buildings) | 도구 Tool | 설명 Description |
- `토목_도로` -- 도로 (Roads) |---|---|
- `토목_상하수도` -- 상하수도 (Water & Sewerage) | `civilplan_parse_project` | 자연어 프로젝트 설명을 구조화된 JSON으로 변환합니다.<br/>Parses a natural-language project brief into structured JSON. |
- `토목_하천` -- 하천 (Rivers) | `civilplan_get_legal_procedures` | 사업 조건에 맞는 인허가·환경 절차를 정리합니다.<br/>Finds permit and environmental procedures for the project. |
- `조경` -- 조경 (Landscaping) | `civilplan_get_phase_checklist` | 단계별 체크리스트를 생성합니다.<br/>Builds phase-by-phase execution checklists. |
- `복합` -- 복합 사업 (Mixed projects) | `civilplan_evaluate_impact_assessments` | 영향평가 필요 여부를 검토합니다.<br/>Evaluates impact-assessment requirements. |
| `civilplan_estimate_quantities` | 개략 물량을 산정합니다.<br/>Estimates conceptual quantities. |
| `civilplan_get_unit_prices` | 지역 보정이 반영된 단가를 조회합니다.<br/>Looks up unit prices with regional adjustments. |
| `civilplan_get_applicable_guidelines` | 적용 대상 설계 기준을 찾습니다.<br/>Finds applicable design guidelines. |
| `civilplan_fetch_guideline_summary` | 기준 전문의 핵심 항목을 요약합니다.<br/>Fetches summaries of guideline references. |
| `civilplan_select_bid_type` | 발주·입찰 방식을 추천합니다.<br/>Recommends a bidding/procurement method. |
| `civilplan_estimate_waste_disposal` | 건설폐기물 물량과 처리비를 계산합니다.<br/>Estimates construction waste volume and disposal cost. |
| `civilplan_query_land_info` | 토지·지목·용도지역 정보를 조회합니다.<br/>Queries land, parcel, and zoning information. |
| `civilplan_analyze_feasibility` | IRR, NPV, DSCR 등 사업성을 계산합니다.<br/>Calculates IRR, NPV, DSCR, and related feasibility metrics. |
| `civilplan_validate_against_benchmark` | 공공 기준이나 벤치마크와 비교합니다.<br/>Checks estimates against public benchmarks. |
### 출력 형식 | Output Formats #### 문서·도면 도구 | Document and Drawing Tools
- **Excel (.xlsx)**: 사업내역서(BOQ), 일정표, 예산 보고서 | 도구 Tool | 설명 Description |
- **Word (.docx)**: 투자계획서(사업계획서) |---|---|
- **SVG**: 평면도, 횡단면도, 종단면도 | `civilplan_generate_boq_excel` | BOQ Excel 파일을 생성합니다.<br/>Generates a BOQ Excel workbook. |
- **DXF**: CAD 호환 도면 | `civilplan_generate_investment_doc` | 투자·사업계획 Word 문서를 생성합니다.<br/>Generates an investment/planning Word document. |
- **JSON**: 모든 도구의 구조화된 응답 데이터 | `civilplan_generate_budget_report` | 예산 보고서를 작성합니다.<br/>Builds a budget report document. |
| `civilplan_generate_schedule` | 일정표 Excel 파일을 생성합니다.<br/>Creates a schedule workbook. |
| `civilplan_generate_svg_drawing` | SVG 개략 도면을 생성합니다.<br/>Generates conceptual SVG drawings. |
| `civilplan_generate_dxf_drawing` | DXF CAD 도면을 생성합니다.<br/>Generates DXF CAD drawings. |
| `civilplan_generate_birdseye_view` | Bird's-Eye / Perspective PNG 렌더를 생성합니다.<br/>Generates bird's-eye and perspective PNG renders. |
--- ### 지원 도메인 | Supported Domains
| 도메인 Domain | 설명 Description |
|---|---|
| `토목_도로` | 도로, 진입로, 포장, 차선 중심 프로젝트<br/>Roads, access roads, pavement, lane-focused projects |
| `건축` | 건물, 복지관, 학교, 오피스 등 건축 프로젝트<br/>Buildings, welfare centers, schools, offices, and similar building projects |
| `토목_상하수도` | 상수도, 하수도, 우수도, 관로 중심 프로젝트<br/>Water, sewer, stormwater, and pipeline-centric projects |
| `토목_하천` | 하천 정비, 제방, 배수, 수변 구조물 프로젝트<br/>River improvement, levee, drainage, and riverside structure projects |
| `조경` | 공원, 녹지, 식재, 휴게 공간 프로젝트<br/>Landscape, parks, planting, and open-space projects |
| `복합` | 다분야가 섞인 복합 개발 프로젝트<br/>Mixed multi-domain development projects |
## 빠른 시작 가이드 | Quick Start Guide ## 빠른 시작 가이드 | Quick Start Guide
### 1단계: 설치 | Step 1: Install ### 1. 저장소 받기 | Clone the Repository
```bash ```bash
# 저장소 클론 | Clone the repository
git clone https://github.com/sinmb79/Construction-project-master.git git clone https://github.com/sinmb79/Construction-project-master.git
cd Construction-project-master cd Construction-project-master
# 가상환경 생성 및 활성화 | Create and activate virtual environment
python -m venv .venv python -m venv .venv
```
# Windows: ### 2. 가상환경 활성화와 패키지 설치 | Activate the Environment and Install Dependencies
```bash
# Windows
.venv\Scripts\activate .venv\Scripts\activate
# macOS/Linux:
# macOS / Linux
source .venv/bin/activate source .venv/bin/activate
# 패키지 설치 | Install dependencies python -m pip install -r requirements.txt
pip install -r requirements.txt
``` ```
### 2단계: API 키 설정 | Step 2: Configure API Keys ### 3. API 키 설정 | Configure API Keys
일부 도구(토지 정보 조회 등)는 공공 API 키가 필요합니다. 없어도 대부분의 기능은 동작합니다. | 방법 Method | 명령 Command | 설명 Description |
|---|---|---|
| `.env` 파일 | `copy .env.example .env` (Windows)<br/>`cp .env.example .env` (macOS/Linux) | 로컬 개발용으로 가장 단순합니다.<br/>The simplest option for local development. |
| 암호화 저장소 | `python setup_keys.py` | Windows DPAPI에 키를 암호화 저장합니다.<br/>Stores keys in Windows DPAPI-encrypted storage. |
Some tools (land info queries, etc.) require public API keys. Most features work without them. `.env` 예시는 아래와 같습니다.
An example `.env` looks like this.
**방법 A: `.env` 파일 | Option A: `.env` file**
```bash
# .env.example을 복사하여 키를 입력합니다
# Copy .env.example and fill in your keys
copy .env.example .env # Windows
cp .env.example .env # macOS/Linux
```
`.env` 파일을 편집하여 키를 입력하세요:
```env ```env
# 공공데이터포털 (https://www.data.go.kr) 에서 발급 DATA_GO_KR_API_KEY=
DATA_GO_KR_API_KEY=your_key_here VWORLD_API_KEY=
GEMINI_API_KEY=
# 브이월드 (https://www.vworld.kr) 에서 발급
VWORLD_API_KEY=your_key_here
``` ```
**방법 B: 암호화 저장 | Option B: Encrypted local storage** ### 4. 서버 실행 | Start the Server
```bash
# 대화형으로 키 입력 | Enter keys interactively
python setup_keys.py
# 또는 기존 .env 파일을 암호화 저장소로 가져오기
# Or import from existing .env file
python setup_keys.py --from-env-file .env
```
> Windows에서는 DPAPI를 사용하여 현재 사용자 프로필에 암호화 저장됩니다.
> On Windows, keys are encrypted with DPAPI under your user profile.
### 3단계: 서버 실행 | Step 3: Start the Server
```bash ```bash
python server.py python server.py
``` ```
서버가 `http://127.0.0.1:8765/mcp`에서 시작됩니다. 실행 주소는 `http://127.0.0.1:8765/mcp`니다.
The server runs at `http://127.0.0.1:8765/mcp`.
The server starts at `http://127.0.0.1:8765/mcp`. ### 5. MCP 클라이언트 연결 | Connect an MCP Client
### 4단계: AI 클라이언트 연결 | Step 4: Connect Your AI Client #### Claude Code
```bash
claude mcp add --transport http civilplan http://127.0.0.1:8765/mcp
```
#### Claude Desktop #### Claude Desktop
`claude_desktop_config.json` (또는 설정 파일)에 다음을 추가하세요: | 항목 Item | 값 Value |
|---|---|
| 서버 유형 Server type | HTTP MCP server |
| URL | `http://127.0.0.1:8765/mcp` |
| Windows 설정 파일 Common Windows config path | `%APPDATA%\Claude\claude_desktop_config.json` |
Add the following to your `claude_desktop_config.json`: HTTP MCP 서버를 추가한 뒤 Claude Desktop을 재시작하세요.
Add the HTTP MCP server and restart Claude Desktop.
```json #### ChatGPT Developer Mode
{
"mcpServers": {
"civilplan": {
"command": "mcp-remote",
"args": ["http://127.0.0.1:8765/mcp"]
}
}
}
```
#### Claude Code (CLI) | 단계 Step | 설명 Description |
|---|---|
| 1 | ChatGPT에서 `Settings → Apps → Advanced settings → Developer mode`를 켭니다.<br/>Enable `Settings → Apps → Advanced settings → Developer mode` in ChatGPT. |
| 2 | `Create app`를 눌러 원격 MCP 서버를 등록합니다.<br/>Click `Create app` to register a remote MCP server. |
| 3 | 로컬 서버는 직접 연결되지 않으므로 터널 URL이 필요합니다.<br/>Local servers cannot be connected directly, so you need a tunnel URL. |
`cloudflared` 예시는 아래와 같습니다.
An example `cloudflared` tunnel command is shown below.
```bash ```bash
claude mcp add civilplan http://127.0.0.1:8765/mcp cloudflared tunnel --url http://127.0.0.1:8765
``` ```
#### ChatGPT (Developer Mode) 터널이 만든 HTTPS URL을 ChatGPT 앱 생성 화면에 넣으세요.
Use the HTTPS URL produced by the tunnel when creating the ChatGPT app.
ChatGPT는 localhost에 직접 연결할 수 없습니다. ngrok 또는 Cloudflare Tunnel을 사용하세요. #### 기타 MCP 클라이언트 | Other MCP Clients
ChatGPT cannot connect to localhost directly. Use ngrok or Cloudflare Tunnel: | 항목 Item | 값 Value |
|---|---|
```bash | 프로토콜 Protocol | Streaming HTTP |
# ngrok으로 서버를 외부에 노출 | URL | `http://127.0.0.1:8765/mcp` |
ngrok http 8765
```
생성된 HTTPS URL을 ChatGPT 설정 -> Connectors -> Create에 입력합니다.
Use the generated HTTPS URL in ChatGPT Settings -> Connectors -> Create.
---
## 실전 사용 예시 | Real-World Usage Examples ## 실전 사용 예시 | Real-World Usage Examples
### 예시 1: 개설(신설) 공사 기획 | Example 1: Planning a New Local Road ### 예시 1: 프로젝트 파싱 | Example 1: Parse a Road Project
아래는 실제로 CivilPlan MCP를 사용하여 생성한 예시입니다. **AI에게 이렇게 말하세요 | Say this to the AI**
Below is a real example generated using CivilPlan MCP. ```text
도로 신설 L=890m B=6m 아스콘 2차선 상하수도 경기도 화성시 2026~2028
#### AI에게 이렇게 말하세요 | Say this to your AI:
```
소로 신설 L=890m B=6m 아스콘 2차선 상하수도 경기도 둔턱지역 2026~2028
``` ```
#### CivilPlan이 자동으로 수행하는 작업 | What CivilPlan does automatically: **호출되는 도구 | Tool called**
**1) 사업 정보 파싱 | Project Parsing** (`civilplan_parse_project`) ```python
civilplan_parse_project(
description="도로 신설 L=890m B=6m 아스콘 2차선 상하수도 경기도 화성시 2026~2028"
)
```
자연어 입력을 구조화된 데이터로 변환합니다: **결과 예시 | Example result**
```json ```json
{ {
"project_id": "PRJ-20260402-001", "project_id": "PRJ-20260404-001",
"project_type": ["도로", "상수도", "하수도"], "domain": "토목_도로",
"sub_domains": ["토목_상하수도"],
"project_type": ["도로", "하수도"],
"road": { "road": {
"class": "소로", "length_m": 890.0,
"length_m": 890,
"width_m": 6.0, "width_m": 6.0,
"lanes": 2, "lanes": 2,
"pavement": "아스콘" "pavement": "아스콘"
}, },
"terrain": "구릉(둔턱)",
"terrain_factor": 1.4,
"region": "경기도", "region": "경기도",
"region_factor": 1.05,
"year_start": 2026, "year_start": 2026,
"year_end": 2028, "year_end": 2028,
"utilities": ["상수도", "하수도"] "parsed_confidence": 0.92
} }
``` ```
**2) 개략 물량 산출 | Quantity Estimation** (`civilplan_estimate_quantities`) ### 예시 2: 인허가 절차 확인 | Example 2: Check Legal Procedures
표준 횡단면 기준으로 주요 물량을 자동 산출합니다: **AI에게 이렇게 말하세요 | Say this to the AI**
``` ```text
도로 포장: 아스콘 표층 523t, 기층 628t 경기도 공공 도로 사업(총사업비 10.67억, 연장 890m)에 필요한 인허가를 정리해줘
토공: 절토 8,000m3, 성토 5,400m3
배수: L형측구 1,780m, 횡단암거 60m
상수도: PE관 DN100 890m, 소화전 3개소
하수도: 오수관 890m, 우수관 890m, 맨홀 37개소
``` ```
**3) 사업비 산출 | Cost Estimation** (`civilplan_generate_boq_excel`) **호출되는 도구 | Tool called**
6개 시트로 구성된 사업내역서 Excel 파일을 생성합니다: ```python
civilplan_get_legal_procedures(
| 시트 Sheet | 내용 Contents | domain="토목_도로",
|-----------|--------------| project_type="도로",
| 사업개요 | 프로젝트 정보, 면책문구 | total_cost_billion=10.67,
| 사업내역서(BOQ) | 8개 대공종별 수량 x 단가 = 금액 (수식 포함) | road_length_m=890,
| 물량산출근거 | 공종별 계산식 (예: 아스콘 표층 = 4,450m2 x 0.05m x 2.35t/m3) | development_area_m2=None,
| 간접비산출 | 설계비 3.5%, 감리비 3.0%, 부대비 2.0%, 예비비 10% | region="경기도",
| 총사업비요약 | 직접공사비 + 간접비 = **약 10.67억원** | has_farmland=False,
| 연도별투자계획 | 2026: 30%, 2027: 50%, 2028: 20% | has_forest=False,
has_river=False,
**4) 법적 절차 확인 | Legal Procedures** (`civilplan_get_legal_procedures`) is_public=True
)
18개 법적 절차를 자동으로 식별하고, 필수/선택 여부, 소요 기간, 근거 법령을 제공합니다:
```
필수 절차 12건, 선택 절차 6건
예상 인허가 소요: 약 18개월
핵심 경로: 도시계획시설결정 -> 개발행위허가 -> 실시계획인가
``` ```
**5) 영향평가 판단 | Impact Assessments** (`civilplan_evaluate_impact_assessments`) **결과 예시 | Example result**
9종 영향평가 대상 여부를 자동 판단합니다:
| 평가 항목 | 대상 여부 | 근거 |
|----------|----------|------|
| 예비타당성조사 | 비대상 | 총사업비 500억 미만 |
| 지방재정투자심사 | **대상** | 총사업비 10.7억 > 10억 |
| 소규모환경영향평가 | **검토 필요** | 개발면적 5,340m2 |
| 재해영향평가 | **경계선** | 개발면적 5,000m2 이상 |
| 매장문화재 지표조사 | **검토 필요** | 개발면적 3,000m2 이상 |
**6) 도면 생성 | Drawing Generation** (`civilplan_generate_svg_drawing`)
평면도와 횡단면도를 SVG 형식으로 생성합니다:
- **평면도**: 도로 중심선, 측점, 관로 배치, 지형(둔턱) 표시, 구조물 위치
- **횡단면도**: 포장 단면(표층->기층->보조기층->동상방지층), 절토/성토 비탈면, 매설 관로
**7) 투자계획서 | Investment Document** (`civilplan_generate_investment_doc`)
위 모든 결과를 종합하여 Word 투자계획서를 자동 생성합니다:
```
표지
목차
제1장 사업 개요 (목적, 위치, 기간)
제2장 사업 규모 및 내용 (도로 현황, 부대시설, 지형)
제3장 사업비 산출 (BOQ 요약, 간접비, 연도별 투자계획)
제4장 법적 절차 및 추진 일정
제5장 기대 효과 및 결론
별첨: 위치도, 횡단면도
```
### 예시 2: 단가 조회 | Example 2: Unit Price Query
```
경기도 지역의 포장 관련 단가를 알려줘
```
AI가 `civilplan_get_unit_prices`를 호출하여 지역계수가 반영된 단가를 조회합니다:
```json ```json
{ {
"item": "아스콘표층(밀입도13mm)", "summary": {
"spec": "t=50mm", "total_procedures": 3,
"unit": "t", "mandatory_count": 2,
"base_price": 96000, "optional_count": 1,
"region_factor": 1.05, "estimated_prep_months": 12,
"adjusted_price": 100800, "critical_path": [
"source": "조달청 표준시장단가 2026 상반기" "도시·군관리계획 결정",
"개발행위허가",
"소규모환경영향평가"
]
},
"timeline_estimate": {
"인허가완료목표": "착공 18개월 전"
}
} }
``` ```
### 예시 3: 단계별 체크리스트 | Example 3: Phase Checklist ### 예시 3: SVG 도면 생성 | Example 3: Generate an SVG Drawing
``` **AI에게 이렇게 말하세요 | Say this to the AI**
도로 공사 단계에서 해야 할 의무사항을 알려줘
```text
위 프로젝트로 개략 평면도 SVG를 만들어줘
``` ```
AI가 `civilplan_get_phase_checklist`를 호출합니다: **호출되는 도구 | Tool called**
``` ```python
[필수] 착공신고 -- 건설산업기본법 제39조, 착공 전 civilplan_generate_svg_drawing(
미이행 시 500만원 이하 과태료 drawing_type="평면도",
[필수] 품질시험계획 수립 -- 미제출 시 기성 지급 불가 project_spec=project_spec,
[필수] 안전관리계획 수립/인가 quantities=quantities,
... scale="1:200",
output_filename="road-plan.svg"
)
``` ```
--- **결과 예시 | Example result**
## 시스템 아키텍처 | System Architecture ```json
{
``` "status": "success",
Claude / ChatGPT / AI Agent "file_path": "output/road-plan.svg",
| MCP Protocol (Streamable HTTP) "drawing_type": "평면도",
v "quantity_sections": ["earthwork", "pavement", "drainage"]
+------------------------------------------+ }
| CivilPlan MCP (FastMCP) |
| |
| parse_project -> JSON |
| get_legal_procedures -> JSON |
| evaluate_impact -> JSON |
| estimate_quantities -> JSON |
| generate_boq_excel -> .xlsx |
| generate_investment -> .docx |
| generate_schedule -> .xlsx |
| generate_svg_drawing -> .svg |
| generate_dxf_drawing -> .dxf |
| ... (19 tools total) |
+--------------------+---------------------+
|
+------v------+ +--------------+
| SQLite DB | | JSON Data |
| unit_prices | | legal_procs |
| legal_procs | | region_facts |
| project_log | | road_stds |
+-------------+ +--------------+
``` ```
--- ### 예시 4: Bird's-Eye View 렌더 생성 | Example 4: Generate a Bird's-Eye Render
**AI에게 이렇게 말하세요 | Say this to the AI**
```text
이 도로 사업을 발표용 3D 조감도와 사람 시점 투시도로 만들어줘
```
**호출되는 도구 | Tool called**
```python
civilplan_generate_birdseye_view(
project_summary="경기도 화성시 도로 신설 890m, 폭 6m, 2차선 아스콘 포장, 상하수도 포함",
project_spec=project_spec,
svg_drawing="<svg>...</svg>",
resolution="2K"
)
```
**결과 예시 | Example result**
```json
{
"status": "success",
"project_id": "PRJ-20260404-001",
"model": "gemini-3-pro-image-preview",
"resolution": "2K",
"reference_image_path": "output/PRJ-20260404-001_reference.png",
"birdseye_view": {
"status": "success",
"path": "output/PRJ-20260404-001_birdseye.png"
},
"perspective_view": {
"status": "success",
"path": "output/PRJ-20260404-001_perspective.png"
}
}
```
## 프로젝트 구조 | Project Structure ## 프로젝트 구조 | Project Structure
``` ```text
Construction-project-master/ Construction-project-master/
|-- server.py # 메인 서버 진입점 | Main server entry point ├─ server.py # 서버 실행 진입점 | Server entrypoint
|-- setup_keys.py # API 키 설정 도구 | API key setup utility ├─ setup_keys.py # 암호화 키 저장 유틸 | Encrypted key setup helper
|-- pyproject.toml # 프로젝트 메타데이터 | Project metadata ├─ requirements.txt # 런타임 의존성 | Runtime dependencies
|-- requirements.txt # 의존성 목록 | Dependencies ├─ pyproject.toml # 패키지 메타데이터 | Package metadata
|-- .env.example # 환경변수 템플릿 | Environment template ├─ README.md # 사용 가이드 | Usage guide
|-- LICENSE # MIT 라이선스 | MIT License ├─ civilplan_mcp/
| │ ├─ __init__.py # 버전 정보 | Version metadata
|-- civilplan_mcp/ # 메인 패키지 | Main package │ ├─ config.py # 설정·경로·API 키 로딩 | Settings, paths, API key loading
| |-- server.py # FastMCP 서버 정의 | FastMCP server definition ├─ models.py # 도메인 enum | Domain enums
| |-- config.py # 설정, 경로, 상수 | Config, paths, constants │ ├─ secure_store.py # DPAPI 키 저장 | DPAPI-backed key store
| |-- models.py # Pydantic 모델 | Pydantic models │ ├─ prompts/
| |-- secure_store.py # 암호화 키 저장 | Encrypted key storage │ └─ birdseye_templates.py # 도메인별 렌더 프롬프트 | Domain-specific render prompts
| |-- tools/ # 19개 MCP 도구 구현 | 19 MCP tool implementations │ ├─ services/
| |-- data/ # JSON 참조 데이터 | JSON reference data │ └─ gemini_image.py # Gemini 이미지 래퍼 | Gemini image wrapper
| |-- db/ # SQLite 스키마 및 시드 | SQLite schema & seeds │ ├─ tools/
| +-- updater/ # 자동 데이터 갱신 | Automated data updaters │ ├─ birdseye_generator.py # 3D 렌더 도구 | 3D rendering tool
| │ │ ├─ drawing_generator.py # SVG 도면 생성 | SVG drawing generator
+-- tests/ # 테스트 스위트 | Test suite │ │ ├─ dxf_generator.py # DXF 도면 생성 | DXF drawing generator
|-- test_smoke.py # 기본 동작 확인 | Basic smoke tests └─ ... # 나머지 MCP 도구 | Remaining MCP tools
|-- test_parser.py # 파서 테스트 | Parser tests │ ├─ data/ # 기준 JSON 데이터 | Reference JSON data
|-- test_legal.py # 법적 절차 테스트 | Legal procedure tests ├─ db/ # SQLite schema/bootstrap | SQLite schema/bootstrap
|-- test_quantities.py # 물량 산출 테스트 | Quantity tests │ └─ updater/ # 데이터 갱신 로직 | Data update logic
|-- test_generators.py # 파일 생성 테스트 | Generator tests └─ tests/
+-- ... # 기타 테스트 | Other tests ├─ test_config_and_secure_store.py
├─ test_gemini_image.py
├─ test_birdseye_templates.py
├─ test_birdseye_generator.py
└─ ... # 전체 회귀 테스트 | Full regression tests
``` ```
--- ## 자주 겪는 문제 | FAQ and Troubleshooting
## 데이터 자동 갱신 | Automated Data Updates | 문제 Problem | 확인 방법 What to Check | 해결 방법 Fix |
|---|---|---|
CivilPlan은 단가/임금/폐기물 처리비 등 참조 데이터의 정기 갱신을 지원합니다: | `GEMINI_API_KEY is not configured` | `.env` 또는 `setup_keys.py` 저장 여부를 확인합니다.<br/>Check `.env` or whether `setup_keys.py` stored the key. | `GEMINI_API_KEY`를 입력하고 서버를 재시작합니다.<br/>Add `GEMINI_API_KEY` and restart the server. |
| ChatGPT에서 localhost 연결 실패 | ChatGPT는 로컬 URL을 직접 쓰지 못합니다.<br/>ChatGPT cannot use a localhost URL directly. | `cloudflared` 또는 `ngrok`로 HTTPS 터널을 노출합니다.<br/>Expose the server through an HTTPS tunnel such as `cloudflared` or `ngrok`. |
CivilPlan supports scheduled updates for reference data (wages, prices, waste rates): | Claude Code에서 도구가 안 보임 | `claude mcp list`로 등록 상태를 확인합니다.<br/>Use `claude mcp list` to verify registration. | `claude mcp add --transport http civilplan http://127.0.0.1:8765/mcp`를 다시 실행합니다.<br/>Re-run the HTTP MCP registration command. |
| SVG 참고 이미지가 반영되지 않음 | `cairosvg` 설치 여부와 SVG 문자열 유효성을 확인합니다.<br/>Check whether `cairosvg` is installed and the SVG string is valid. | 잘못된 SVG면 텍스트 전용 렌더로 fallback 됩니다.<br/>If SVG conversion fails, the tool falls back to text-only rendering. |
| 시기 Timing | 갱신 항목 Update Item | | 전체 테스트를 다시 돌리고 싶음 | 아래 명령을 사용합니다.<br/>Use the following command. | `python -m pytest tests/ -q` |
|------------|---------------------|
| 1월 2일 09:00 | 상반기 임금, 폐기물 처리비, 간접비율 |
| 7월 10일 09:00 | 하반기 표준시장단가, 간접비율 |
| 9월 2일 09:00 | 하반기 임금 |
갱신 실패 시 `.update_required_*` 플래그 파일이 생성되고, 서버 시작 시 경고가 표시됩니다.
If an update fails, `.update_required_*` flag files are created and startup warnings are shown.
---
## 토지 정보 데이터 설정 | Land Price Data Setup
토지 가격 조회 기능을 사용하려면 수동으로 데이터를 다운로드해야 합니다:
To use land price lookup, manually download data files:
1. 국토교통부 또는 한국부동산원에서 공시지가 CSV/TSV 파일 다운로드
2. `civilplan_mcp/data/land_prices/` 폴더에 넣기
3. UTF-8, CP949, EUC-KR 인코딩 모두 지원
```
civilplan_mcp/data/land_prices/
(여기에 CSV/TSV/ZIP 파일을 넣으세요)
(Place your CSV/TSV/ZIP files here)
```
---
## 테스트 실행 | Running Tests
```bash
pytest tests -q
```
모든 테스트는 외부 API 키 없이도 실행 가능합니다 (로컬 폴백 사용).
All tests run without external API keys (using local fallbacks).
---
## 알려진 제한사항 | Known Limitations ## 알려진 제한사항 | Known Limitations
- **개략 산출**: 모든 사업비/물량은 기획 단계용 개략 산출이며, 실시설계를 대체하지 않습니다 (+-20~30% 오차 가능). | 항목 Item | 설명 Description |
*All estimates are preliminary (+-20-30% variance) and do not replace detailed design.* |---|---|
| 기획 단계 정확도 | 모든 수치와 절차는 개략 검토용입니다.<br/>All numbers and procedures are intended for conceptual planning only. |
| 3D 렌더 의존성 | `civilplan_generate_birdseye_view`는 인터넷 연결과 `GEMINI_API_KEY`가 필요합니다.<br/>`civilplan_generate_birdseye_view` requires internet access and a `GEMINI_API_KEY`. |
| 토지 정보 | 일부 토지 데이터는 외부 API 상태에 따라 결과가 달라질 수 있습니다.<br/>Some land information depends on external API availability. |
| 조경·복합 도메인 | 프롬프트와 절차 데이터가 계속 보강 중입니다.<br/>Landscape and mixed-domain support is still being expanded. |
| 공개 제출 문서 | 생성 결과는 공식 제출 문서가 아닙니다.<br/>Generated outputs are not valid submission documents. |
- **토지 용도 데이터**: 외부 서비스 불안정으로 일부 필지의 용도지역 정보가 불완전할 수 있습니다. ## 면책사항 | Disclaimer
*External land-use services can be unstable; some parcels may return partial zoning data.*
- **공시지가 조회**: 수동 다운로드 필요 (`civilplan_mcp/data/land_prices/`). > 본 저장소의 결과물은 기획 단계 참고자료이며, 상세 설계·발주·공식 제출용 문서를 대체하지 않습니다.
*Land price lookup requires manually downloaded source files.* > Outputs from this repository are planning-stage references and do not replace detailed design, procurement, or official submission documents.
- **나라장터 벤치마크**: 공공 API가 불안정하여 로컬 휴리스틱으로 폴백합니다.
*Nara benchmark validation falls back to local heuristics when the public API is unavailable.*
- **조경 분야**: 법적 절차 데이터가 아직 완전하지 않습니다.
*Landscape-specific legal/procedure data is not fully implemented yet.*
---
## 면책 조항 | Disclaimer
> 본 도구의 산출 결과는 **기획 단계 참고용**이며, 실시설계/시공을 위한 공식 문서로 사용할 수 없습니다.
> 실제 사업 집행 시에는 반드시 관련 분야 전문가의 검토를 받으시기 바랍니다.
>
> All outputs are for **preliminary planning reference only** and cannot be used as official documents for detailed design or construction.
> Please consult qualified professionals before executing any actual project.
---
## 라이선스 | License ## 라이선스 | License
MIT License -- 자유롭게 사용, 수정, 배포할 수 있습니다. | 항목 Item | 내용 Detail |
|---|---|
MIT License -- Free to use, modify, and distribute. | 라이선스 License | MIT |
| 사용 범위 Usage | 사용, 수정, 배포 가능<br/>Free to use, modify, and distribute |
---
## 만든 사람 | Author ## 만든 사람 | Author
**22B Labs** (sinmb79) | 항목 Item | 내용 Detail |
|---|---|
문의사항이나 기여는 [Issues](https://github.com/sinmb79/Construction-project-master/issues)를 이용해 주세요. | 팀 Team | **22B Labs** |
| 저장소 Repository | [sinmb79/Construction-project-master](https://github.com/sinmb79/Construction-project-master) |
For questions or contributions, please use [Issues](https://github.com/sinmb79/Construction-project-master/issues). | 문의 Contact | [Issues](https://github.com/sinmb79/Construction-project-master/issues) |

View File

@@ -1,3 +1,3 @@
__all__ = ["__version__"] __all__ = ["__version__"]
__version__ = "1.0.0" __version__ = "2.0.0"

View File

@@ -28,7 +28,7 @@ def _load_secure_api_keys(path: Path) -> dict[str, str]:
class Settings(BaseModel): class Settings(BaseModel):
app_name: str = "civilplan_mcp" app_name: str = "civilplan_mcp"
version: str = "1.0.0" version: str = "2.0.0"
host: str = "127.0.0.1" host: str = "127.0.0.1"
port: int = 8765 port: int = 8765
http_path: str = "/mcp" http_path: str = "/mcp"
@@ -38,6 +38,7 @@ class Settings(BaseModel):
key_store_path: Path = Field(default_factory=default_key_store_path) key_store_path: Path = Field(default_factory=default_key_store_path)
data_go_kr_api_key: str = Field(default_factory=lambda: os.getenv("DATA_GO_KR_API_KEY", "")) data_go_kr_api_key: str = Field(default_factory=lambda: os.getenv("DATA_GO_KR_API_KEY", ""))
vworld_api_key: str = Field(default_factory=lambda: os.getenv("VWORLD_API_KEY", "")) vworld_api_key: str = Field(default_factory=lambda: os.getenv("VWORLD_API_KEY", ""))
gemini_api_key: str = Field(default_factory=lambda: os.getenv("GEMINI_API_KEY", ""))
@lru_cache(maxsize=1) @lru_cache(maxsize=1)
@@ -50,6 +51,8 @@ def get_settings() -> Settings:
settings.data_go_kr_api_key = secure_keys.get("DATA_GO_KR_API_KEY", "") settings.data_go_kr_api_key = secure_keys.get("DATA_GO_KR_API_KEY", "")
if not settings.vworld_api_key: if not settings.vworld_api_key:
settings.vworld_api_key = secure_keys.get("VWORLD_API_KEY", "") settings.vworld_api_key = secure_keys.get("VWORLD_API_KEY", "")
if not settings.gemini_api_key:
settings.gemini_api_key = secure_keys.get("GEMINI_API_KEY", "")
settings.output_dir.mkdir(parents=True, exist_ok=True) settings.output_dir.mkdir(parents=True, exist_ok=True)
return settings return settings
@@ -62,4 +65,6 @@ def check_api_keys() -> list[str]:
missing.append("DATA_GO_KR_API_KEY") missing.append("DATA_GO_KR_API_KEY")
if not settings.vworld_api_key: if not settings.vworld_api_key:
missing.append("VWORLD_API_KEY") missing.append("VWORLD_API_KEY")
if not settings.gemini_api_key:
missing.append("GEMINI_API_KEY")
return missing return missing

View File

@@ -0,0 +1,3 @@
from civilplan_mcp.prompts.birdseye_templates import DOMAIN_PROMPTS, VIEW_INSTRUCTIONS, build_prompt
__all__ = ["DOMAIN_PROMPTS", "VIEW_INSTRUCTIONS", "build_prompt"]

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@@ -0,0 +1,67 @@
from __future__ import annotations
from typing import Any
DOMAIN_PROMPTS: dict[str, str] = {
"road": (
"Focus on the road alignment, lane markings, shoulders, drainage channels, utility corridors, "
"guard rails, and the surrounding Korean rural or suburban context."
),
"building": (
"Focus on the building massing, facade materials, rooftop equipment, parking, pedestrian circulation, "
"and the surrounding Korean urban block."
),
"water": (
"Focus on pipeline routing, manholes, pump stations, treatment structures, trench alignment, "
"and road-side utility coordination."
),
"river": (
"Focus on embankments, flood-control structures, riprap, levee walks, bridge crossings, "
"and natural riparian vegetation."
),
"landscape": (
"Focus on planting composition, trails, plazas, seating, play areas, water features, "
"and seasonal Korean vegetation."
),
"mixed": (
"Show a comprehensive development site where roads, buildings, utility systems, and landscape work together "
"as one coordinated Korean construction project."
),
}
VIEW_INSTRUCTIONS: dict[str, str] = {
"birdseye": (
"Create a photorealistic bird's-eye view rendering with an aerial camera angle around 45 to 60 degrees, "
"covering the full project extent and nearby context."
),
"perspective": (
"Create a photorealistic perspective rendering from a representative human-scale viewpoint, "
"showing how the project feels on the ground."
),
}
def build_prompt(
*,
view_type: str,
project_type: str,
project_summary: str,
details: dict[str, Any],
) -> str:
view_instruction = VIEW_INSTRUCTIONS.get(view_type, VIEW_INSTRUCTIONS["birdseye"])
domain_instruction = DOMAIN_PROMPTS.get(project_type, DOMAIN_PROMPTS["mixed"])
detail_lines = [f"- {key}: {value}" for key, value in details.items() if value not in (None, "", [], {})]
detail_block = "\n".join(detail_lines) if detail_lines else "- No additional technical details provided."
return (
f"{view_instruction}\n\n"
f"Project summary:\n{project_summary}\n\n"
f"Technical details:\n{detail_block}\n\n"
f"Domain guidance:\n{domain_instruction}\n\n"
"Style requirements:\n"
"- Professional architectural visualization for a Korean civil or building project.\n"
"- Clear daytime weather, realistic materials, and readable spatial hierarchy.\n"
"- Include surrounding terrain, access roads, and scale cues where appropriate.\n"
"- Avoid people-heavy staging, exaggerated concept-art effects, or fantasy aesthetics."
)

View File

@@ -16,6 +16,7 @@ from civilplan_mcp import __version__
from civilplan_mcp.config import check_api_keys, get_settings from civilplan_mcp.config import check_api_keys, get_settings
from civilplan_mcp.tools.benchmark_validator import validate_against_benchmark from civilplan_mcp.tools.benchmark_validator import validate_against_benchmark
from civilplan_mcp.tools.bid_type_selector import select_bid_type from civilplan_mcp.tools.bid_type_selector import select_bid_type
from civilplan_mcp.tools.birdseye_generator import generate_birdseye_view
from civilplan_mcp.tools.boq_generator import generate_boq_excel from civilplan_mcp.tools.boq_generator import generate_boq_excel
from civilplan_mcp.tools.budget_report_generator import generate_budget_report from civilplan_mcp.tools.budget_report_generator import generate_budget_report
from civilplan_mcp.tools.doc_generator import generate_investment_doc from civilplan_mcp.tools.doc_generator import generate_investment_doc
@@ -113,6 +114,7 @@ def build_mcp() -> FastMCP:
_register_read_tool(app, "civilplan_validate_against_benchmark", validate_against_benchmark) _register_read_tool(app, "civilplan_validate_against_benchmark", validate_against_benchmark)
_register_write_tool(app, "civilplan_generate_budget_report", generate_budget_report) _register_write_tool(app, "civilplan_generate_budget_report", generate_budget_report)
_register_write_tool(app, "civilplan_generate_dxf_drawing", generate_dxf_drawing) _register_write_tool(app, "civilplan_generate_dxf_drawing", generate_dxf_drawing)
_register_write_tool(app, "civilplan_generate_birdseye_view", generate_birdseye_view)
return app return app

View File

@@ -0,0 +1,3 @@
from civilplan_mcp.services.gemini_image import GeminiImageService
__all__ = ["GeminiImageService"]

View File

@@ -0,0 +1,122 @@
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from PIL import Image as PILImage
try:
from google import genai
from google.genai import types as genai_types
except ImportError: # pragma: no cover - exercised in tests via runtime guard
genai = None
genai_types = None
logger = logging.getLogger(__name__)
class GeminiImageService:
def __init__(
self,
*,
api_key: str,
model: str = "gemini-3-pro-image-preview",
client: Any | None = None,
) -> None:
self.api_key = api_key
self.model = model
self._client = client or self._build_client()
def _build_client(self) -> Any:
if genai is None:
raise RuntimeError("google-genai is not installed. Install it to use GeminiImageService.")
return genai.Client(api_key=self.api_key)
def _build_config(self, *, aspect_ratio: str, image_size: str) -> Any:
if genai_types is None:
return {
"response_modalities": ["TEXT", "IMAGE"],
"image_config": {
"aspect_ratio": aspect_ratio,
"image_size": image_size,
},
}
image_config_factory = getattr(genai_types, "ImageConfig", None)
generate_config_factory = getattr(genai_types, "GenerateContentConfig", None)
image_config = (
image_config_factory(aspect_ratio=aspect_ratio, image_size=image_size)
if callable(image_config_factory)
else {
"aspect_ratio": aspect_ratio,
"image_size": image_size,
}
)
if callable(generate_config_factory):
return generate_config_factory(
response_modalities=["TEXT", "IMAGE"],
image_config=image_config,
)
return {
"response_modalities": ["TEXT", "IMAGE"],
"image_config": image_config,
}
@staticmethod
def _extract_parts(response: Any) -> list[Any]:
direct_parts = getattr(response, "parts", None)
if direct_parts:
return list(direct_parts)
candidates = getattr(response, "candidates", None) or []
for candidate in candidates:
candidate_parts = getattr(getattr(candidate, "content", None), "parts", None)
if candidate_parts:
return list(candidate_parts)
return []
def generate_image(
self,
*,
prompt: str,
output_path: str,
reference_image_path: str | None = None,
aspect_ratio: str = "16:9",
image_size: str = "2K",
) -> dict[str, str]:
reference_image: PILImage.Image | None = None
try:
contents: list[Any] = [prompt]
if reference_image_path:
reference_image = PILImage.open(reference_image_path)
contents.append(reference_image)
response = self._client.models.generate_content(
model=self.model,
contents=contents,
config=self._build_config(aspect_ratio=aspect_ratio, image_size=image_size),
)
for part in self._extract_parts(response):
if getattr(part, "inline_data", None) is not None and hasattr(part, "as_image"):
output = Path(output_path)
output.parent.mkdir(parents=True, exist_ok=True)
part.as_image().save(str(output))
return {"status": "success", "path": str(output)}
text_parts = [str(part.text).strip() for part in self._extract_parts(response) if getattr(part, "text", None)]
message = "No image in API response."
if text_parts:
message = f"{message} {' '.join(text_parts)}"
return {"status": "error", "error": message}
except Exception as exc:
logger.exception("Gemini image generation failed.")
return {"status": "error", "error": str(exc)}
finally:
if reference_image is not None:
reference_image.close()

View File

@@ -42,11 +42,13 @@ def main(argv: list[str] | None = None) -> int:
data_go_kr_api_key = _prompt_value("DATA_GO_KR_API_KEY", imported.get("DATA_GO_KR_API_KEY", "")) data_go_kr_api_key = _prompt_value("DATA_GO_KR_API_KEY", imported.get("DATA_GO_KR_API_KEY", ""))
vworld_api_key = _prompt_value("VWORLD_API_KEY", imported.get("VWORLD_API_KEY", "")) vworld_api_key = _prompt_value("VWORLD_API_KEY", imported.get("VWORLD_API_KEY", ""))
gemini_api_key = _prompt_value("GEMINI_API_KEY", imported.get("GEMINI_API_KEY", ""))
target = save_api_keys( target = save_api_keys(
{ {
"DATA_GO_KR_API_KEY": data_go_kr_api_key, "DATA_GO_KR_API_KEY": data_go_kr_api_key,
"VWORLD_API_KEY": vworld_api_key, "VWORLD_API_KEY": vworld_api_key,
"GEMINI_API_KEY": gemini_api_key,
} }
) )

View File

@@ -0,0 +1,129 @@
from __future__ import annotations
import logging
from pathlib import Path
from typing import Any
from civilplan_mcp.config import get_settings
from civilplan_mcp.models import ProjectDomain
from civilplan_mcp.prompts.birdseye_templates import build_prompt
from civilplan_mcp.services.gemini_image import GeminiImageService
from civilplan_mcp.tools._base import wrap_response
logger = logging.getLogger(__name__)
DOMAIN_TO_PROJECT_TYPE = {
"토목_도로": "road",
"건축": "building",
"토목_상하수도": "water",
"토목_하천": "river",
"조경": "landscape",
"복합": "mixed",
}
def _domain_to_project_type(domain: str) -> str:
return DOMAIN_TO_PROJECT_TYPE.get(domain, "mixed")
def _resolve_domain(domain: str | None) -> ProjectDomain:
try:
return ProjectDomain(domain or ProjectDomain.복합.value)
except ValueError:
return ProjectDomain.복합
def svg_to_png(svg_content: str, output_path: str) -> str:
import cairosvg
cairosvg.svg2png(bytestring=svg_content.encode("utf-8"), write_to=output_path)
return output_path
def generate_birdseye_view(
*,
project_summary: str,
project_spec: dict[str, Any],
svg_drawing: str | None = None,
resolution: str = "2K",
) -> dict[str, Any]:
settings = get_settings()
domain = _resolve_domain(project_spec.get("domain"))
project_id = project_spec.get("project_id", "birdseye-render")
if not settings.gemini_api_key:
return wrap_response(
{
"status": "error",
"project_id": project_id,
"error": "GEMINI_API_KEY is not configured. Add it to .env or store it with python setup_keys.py.",
},
domain,
)
output_dir = Path(project_spec.get("output_dir") or settings.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
details: dict[str, Any] = {}
if isinstance(project_spec.get("road"), dict):
details.update({key: value for key, value in project_spec["road"].items() if value is not None})
for key in ("terrain", "region", "utilities", "year_start", "year_end"):
value = project_spec.get(key)
if value not in (None, "", [], {}):
details[key] = value
reference_image_path: str | None = None
if svg_drawing:
try:
reference_image_path = svg_to_png(svg_drawing, str(output_dir / f"{project_id}_reference.png"))
except Exception as exc:
logger.warning("Failed to convert SVG reference for birdseye render: %s", exc)
service = GeminiImageService(api_key=settings.gemini_api_key)
project_type = _domain_to_project_type(domain.value)
birdseye_result = service.generate_image(
prompt=build_prompt(
view_type="birdseye",
project_type=project_type,
project_summary=project_summary,
details=details,
),
output_path=str(output_dir / f"{project_id}_birdseye.png"),
reference_image_path=reference_image_path,
aspect_ratio="16:9",
image_size=resolution,
)
perspective_result = service.generate_image(
prompt=build_prompt(
view_type="perspective",
project_type=project_type,
project_summary=project_summary,
details=details,
),
output_path=str(output_dir / f"{project_id}_perspective.png"),
reference_image_path=reference_image_path,
aspect_ratio="16:9",
image_size=resolution,
)
if birdseye_result["status"] == "success" and perspective_result["status"] == "success":
status = "success"
elif birdseye_result["status"] == "success" or perspective_result["status"] == "success":
status = "partial"
else:
status = "error"
return wrap_response(
{
"status": status,
"project_id": project_id,
"model": service.model,
"resolution": resolution,
"reference_image_path": reference_image_path,
"birdseye_view": birdseye_result,
"perspective_view": perspective_result,
},
domain,
)

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@@ -0,0 +1,158 @@
# CivilPlan MCP v2 - Bird's-Eye View Generation Design Spec
## Overview
Add a new MCP tool `generate_birdseye_view` to CivilPlan MCP that generates 3D architectural/civil engineering bird's-eye view and perspective renderings using Google's Nano Banana Pro (Gemini 3 Pro Image) API. Additionally, remove all local LLM dependencies and create a polished release with comprehensive documentation.
## Scope
### In Scope
1. **New MCP tool**: `generate_birdseye_view` — generates 2 images (bird's-eye + perspective)
2. **Nano Banana Pro integration** via `google-genai` Python SDK
3. **Project-type-specific prompt templates** (road, building, water/sewerage, river, landscaping)
4. **Local LLM removal** — delete all local LLM code and dependencies
5. **Release v2.0.0** — GitHub release with detailed README and connection guides
### Out of Scope
- Night/day or seasonal variations
- Video/animation generation
- 3D model file export (OBJ, FBX, etc.)
## Architecture
### Data Flow
```
MCP Client (Claude / ChatGPT)
|
| MCP Protocol (HTTP)
v
CivilPlan MCP Server (FastMCP)
|
| generate_birdseye_view tool called
v
BirdseyeViewGenerator
|
|-- [If SVG drawing exists] Convert SVG to PNG reference image
|-- [Always] Build optimized prompt from project data
|
v
Google Gemini API (Nano Banana Pro model)
|
v
2x PNG images returned (bird's-eye + perspective)
|
|-- Save to output directory
|-- Return base64 + file paths via MCP response
```
### New Files
| File | Purpose |
|------|---------|
| `civilplan_mcp/tools/birdseye_generator.py` | MCP tool implementation |
| `civilplan_mcp/prompts/birdseye_templates.py` | Project-type prompt templates |
| `civilplan_mcp/services/gemini_image.py` | Nano Banana Pro API client wrapper |
| `tests/test_birdseye_generator.py` | Unit tests |
### Tool Interface
```python
@mcp.tool()
async def generate_birdseye_view(
project_summary: str, # Parsed project description (from project_parser)
project_type: str, # "road" | "building" | "water" | "river" | "landscape" | "mixed"
svg_drawing: str | None, # Optional SVG drawing content from drawing_generator
resolution: str = "2k", # "2k" | "4k"
output_dir: str = "./output/renders"
) -> dict:
"""
Returns:
{
"birdseye_view": {"path": str, "base64": str},
"perspective_view": {"path": str, "base64": str},
"prompt_used": str,
"model": "nano-banana-pro"
}
"""
```
### Prompt Template Strategy
Each project type gets a specialized prompt template:
- **Road**: Emphasize road alignment, terrain, surrounding land use, utility corridors
- **Building**: Emphasize building mass, facade, site context, parking/landscaping
- **Water/Sewerage**: Emphasize pipeline routes, treatment facilities, connection points
- **River**: Emphasize riverbank, embankments, bridges, flood plains
- **Landscape**: Emphasize vegetation, pathways, public spaces, terrain grading
- **Mixed**: Combine relevant elements from applicable types
Template format:
```
"Create a photorealistic {view_type} of a {project_type} project:
{project_details}
Style: Professional architectural visualization, Korean construction context,
clear weather, daytime, {resolution} resolution"
```
### API Configuration
- API key stored via existing `.env` / `secure_store.py` pattern
- New env var: `GEMINI_API_KEY`
- SDK: `google-genai` (official Google Gen AI Python SDK)
- Model: `gemini-3-pro-image` (Nano Banana Pro)
- Error handling: On API failure, return error message without crashing the MCP tool
### SVG-to-PNG Conversion
When an SVG drawing is provided as reference:
1. Convert SVG to PNG using `cairosvg` or `Pillow`
2. Send as reference image alongside the text prompt
3. Nano Banana Pro uses it for spatial understanding
### Local LLM Removal
Identify and remove:
- Any local model loading code (transformers, llama-cpp, ollama, etc.)
- Related dependencies in `requirements.txt` / `pyproject.toml`
- Config entries referencing local models
- Replace with Gemini API calls where needed
## Release Plan
### Version: v2.0.0
### README Overhaul
- Project overview with feature highlights
- Quick start guide (clone, install, configure, run)
- Tool reference table (all 20 tools including new birdseye)
- Claude Desktop connection guide (step-by-step with screenshots description)
- ChatGPT / OpenAI connection guide
- API key setup guide (Gemini, public data portal)
- Example outputs (birdseye rendering description)
- Troubleshooting FAQ
### GitHub Release
- Tag: `v2.0.0`
- Release notes summarizing changes
- Installation instructions
## Testing Strategy
- Unit test for prompt template generation
- Unit test for SVG-to-PNG conversion
- Integration test with mocked Gemini API response
- Manual end-to-end test with real API key
## Dependencies Added
| Package | Purpose |
|---------|---------|
| `google-genai` | Gemini API SDK (Nano Banana Pro) |
| `cairosvg` | SVG to PNG conversion |
| `Pillow` | Image processing |
## Dependencies Removed
All local LLM packages (to be identified during implementation by scanning current requirements).

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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project] [project]
name = "civilplan-mcp" name = "civilplan-mcp"
version = "1.0.0" version = "2.0.0"
description = "CivilPlan MCP server for Korean civil and building project planning." description = "CivilPlan MCP server for Korean civil and building project planning."
readme = "README.md" readme = "README.md"
requires-python = ">=3.11" requires-python = ">=3.11"
@@ -24,6 +24,9 @@ dependencies = [
"apscheduler>=3.10.0", "apscheduler>=3.10.0",
"python-dotenv>=1.0.1", "python-dotenv>=1.0.1",
"python-dateutil>=2.9.0", "python-dateutil>=2.9.0",
"google-genai>=1.0.0",
"cairosvg>=2.7.0",
"Pillow>=10.0.0",
] ]
[project.scripts] [project.scripts]

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@@ -9,4 +9,7 @@ httpx>=0.27.0
apscheduler>=3.10.0 apscheduler>=3.10.0
python-dotenv>=1.0.1 python-dotenv>=1.0.1
python-dateutil>=2.9.0 python-dateutil>=2.9.0
google-genai>=1.0.0
cairosvg>=2.7.0
Pillow>=10.0.0
pytest>=8.0.0 pytest>=8.0.0

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@@ -0,0 +1,142 @@
from __future__ import annotations
from unittest.mock import MagicMock
from civilplan_mcp.tools.project_parser import parse_project
def _sample_project_spec(tmp_path) -> dict:
project_spec = parse_project(
description="도로 신설 L=890m B=6m 아스콘 2차선 상하수도 경기도 화성시 2026~2028"
)
project_spec["project_id"] = "PRJ-20260404-001"
project_spec["output_dir"] = str(tmp_path)
return project_spec
def test_generate_birdseye_view_returns_both_images(monkeypatch, tmp_path) -> None:
from civilplan_mcp.tools import birdseye_generator
mock_service = MagicMock()
mock_service.generate_image.side_effect = lambda **kwargs: {
"status": "success",
"path": kwargs["output_path"],
}
monkeypatch.setattr(
birdseye_generator,
"get_settings",
lambda: MagicMock(gemini_api_key="test-key", output_dir=tmp_path),
)
monkeypatch.setattr(
birdseye_generator,
"GeminiImageService",
lambda **kwargs: mock_service,
)
result = birdseye_generator.generate_birdseye_view(
project_summary="경기도 화성시 도로 신설 890m",
project_spec=_sample_project_spec(tmp_path),
)
assert result["status"] == "success"
assert result["birdseye_view"]["status"] == "success"
assert result["perspective_view"]["status"] == "success"
assert mock_service.generate_image.call_count == 2
assert "validity_disclaimer" in result
def test_generate_birdseye_view_uses_svg_reference(monkeypatch, tmp_path) -> None:
from civilplan_mcp.tools import birdseye_generator
reference_path = tmp_path / "reference.png"
reference_path.write_bytes(b"png")
mock_service = MagicMock()
mock_service.generate_image.return_value = {"status": "success", "path": str(tmp_path / "out.png")}
monkeypatch.setattr(
birdseye_generator,
"get_settings",
lambda: MagicMock(gemini_api_key="test-key", output_dir=tmp_path),
)
monkeypatch.setattr(
birdseye_generator,
"GeminiImageService",
lambda **kwargs: mock_service,
)
monkeypatch.setattr(
birdseye_generator,
"svg_to_png",
lambda svg_content, output_path: str(reference_path),
)
result = birdseye_generator.generate_birdseye_view(
project_summary="경기도 화성시 도로 신설 890m",
project_spec=_sample_project_spec(tmp_path),
svg_drawing="<svg></svg>",
)
assert result["status"] == "success"
for call in mock_service.generate_image.call_args_list:
assert call.kwargs["reference_image_path"] == str(reference_path)
def test_generate_birdseye_view_requires_gemini_key(monkeypatch, tmp_path) -> None:
from civilplan_mcp.tools import birdseye_generator
monkeypatch.setattr(
birdseye_generator,
"get_settings",
lambda: MagicMock(gemini_api_key="", output_dir=tmp_path),
)
result = birdseye_generator.generate_birdseye_view(
project_summary="경기도 화성시 도로 신설 890m",
project_spec=_sample_project_spec(tmp_path),
)
assert result["status"] == "error"
assert "GEMINI_API_KEY" in result["error"]
def test_generate_birdseye_view_returns_partial_if_one_view_fails(monkeypatch, tmp_path) -> None:
from civilplan_mcp.tools import birdseye_generator
mock_service = MagicMock()
mock_service.generate_image.side_effect = [
{"status": "success", "path": str(tmp_path / "birdseye.png")},
{"status": "error", "error": "rate limit"},
]
monkeypatch.setattr(
birdseye_generator,
"get_settings",
lambda: MagicMock(gemini_api_key="test-key", output_dir=tmp_path),
)
monkeypatch.setattr(
birdseye_generator,
"GeminiImageService",
lambda **kwargs: mock_service,
)
result = birdseye_generator.generate_birdseye_view(
project_summary="경기도 화성시 도로 신설 890m",
project_spec=_sample_project_spec(tmp_path),
)
assert result["status"] == "partial"
assert result["birdseye_view"]["status"] == "success"
assert result["perspective_view"]["status"] == "error"
def test_domain_to_project_type_mapping() -> None:
from civilplan_mcp.tools.birdseye_generator import _domain_to_project_type
assert _domain_to_project_type("토목_도로") == "road"
assert _domain_to_project_type("건축") == "building"
assert _domain_to_project_type("토목_상하수도") == "water"
assert _domain_to_project_type("토목_하천") == "river"
assert _domain_to_project_type("조경") == "landscape"
assert _domain_to_project_type("복합") == "mixed"
assert _domain_to_project_type("unknown") == "mixed"

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@@ -0,0 +1,51 @@
from __future__ import annotations
def test_build_birdseye_prompt_for_road() -> None:
from civilplan_mcp.prompts.birdseye_templates import build_prompt
prompt = build_prompt(
view_type="birdseye",
project_type="road",
project_summary="경기도 지방도 890m, 폭 6m, 2차선 아스팔트 포장 도로",
details={"length_m": 890, "width_m": 6, "lanes": 2, "pavement": "아스팔트"},
)
assert "bird's-eye view" in prompt.lower()
assert "890" in prompt
assert "road" in prompt.lower()
def test_build_perspective_prompt_for_building() -> None:
from civilplan_mcp.prompts.birdseye_templates import build_prompt
prompt = build_prompt(
view_type="perspective",
project_type="building",
project_summary="서울시 강남구 5층 오피스 빌딩",
details={"floors": 5, "use": "오피스"},
)
assert "perspective" in prompt.lower()
assert "building" in prompt.lower()
def test_all_project_types_have_templates() -> None:
from civilplan_mcp.prompts.birdseye_templates import DOMAIN_PROMPTS
assert set(DOMAIN_PROMPTS) == {"road", "building", "water", "river", "landscape", "mixed"}
def test_unknown_project_type_falls_back_to_mixed() -> None:
from civilplan_mcp.prompts.birdseye_templates import build_prompt
prompt = build_prompt(
view_type="birdseye",
project_type="unknown",
project_summary="복합 개발 프로젝트",
details={},
)
assert isinstance(prompt, str)
assert len(prompt) > 50
assert "comprehensive development site" in prompt.lower()

View File

@@ -45,15 +45,18 @@ def test_get_settings_uses_secure_store_when_env_missing(tmp_path: Path, monkeyp
lambda path: { lambda path: {
"DATA_GO_KR_API_KEY": "secure-data-key", "DATA_GO_KR_API_KEY": "secure-data-key",
"VWORLD_API_KEY": "secure-vworld-key", "VWORLD_API_KEY": "secure-vworld-key",
"GEMINI_API_KEY": "secure-gemini-key",
}, },
) )
monkeypatch.delenv("DATA_GO_KR_API_KEY", raising=False) monkeypatch.delenv("DATA_GO_KR_API_KEY", raising=False)
monkeypatch.delenv("VWORLD_API_KEY", raising=False) monkeypatch.delenv("VWORLD_API_KEY", raising=False)
monkeypatch.delenv("GEMINI_API_KEY", raising=False)
settings = config.get_settings() settings = config.get_settings()
assert settings.data_go_kr_api_key == "secure-data-key" assert settings.data_go_kr_api_key == "secure-data-key"
assert settings.vworld_api_key == "secure-vworld-key" assert settings.vworld_api_key == "secure-vworld-key"
assert settings.gemini_api_key == "secure-gemini-key"
def test_get_settings_prefers_env_values_over_secure_store(tmp_path: Path, monkeypatch) -> None: def test_get_settings_prefers_env_values_over_secure_store(tmp_path: Path, monkeypatch) -> None:
@@ -65,12 +68,37 @@ def test_get_settings_prefers_env_values_over_secure_store(tmp_path: Path, monke
lambda path: { lambda path: {
"DATA_GO_KR_API_KEY": "secure-data-key", "DATA_GO_KR_API_KEY": "secure-data-key",
"VWORLD_API_KEY": "secure-vworld-key", "VWORLD_API_KEY": "secure-vworld-key",
"GEMINI_API_KEY": "secure-gemini-key",
}, },
) )
monkeypatch.setenv("DATA_GO_KR_API_KEY", "env-data-key") monkeypatch.setenv("DATA_GO_KR_API_KEY", "env-data-key")
monkeypatch.setenv("VWORLD_API_KEY", "env-vworld-key") monkeypatch.setenv("VWORLD_API_KEY", "env-vworld-key")
monkeypatch.setenv("GEMINI_API_KEY", "env-gemini-key")
settings = config.get_settings() settings = config.get_settings()
assert settings.data_go_kr_api_key == "env-data-key" assert settings.data_go_kr_api_key == "env-data-key"
assert settings.vworld_api_key == "env-vworld-key" assert settings.vworld_api_key == "env-vworld-key"
assert settings.gemini_api_key == "env-gemini-key"
def test_settings_has_gemini_api_key(monkeypatch) -> None:
monkeypatch.delenv("GEMINI_API_KEY", raising=False)
settings = config.Settings()
assert hasattr(settings, "gemini_api_key")
assert settings.gemini_api_key == ""
def test_check_api_keys_includes_gemini_when_missing(tmp_path: Path, monkeypatch) -> None:
monkeypatch.setattr(config, "BASE_DIR", tmp_path)
monkeypatch.setattr(config, "load_local_env", lambda: None)
monkeypatch.setattr(config, "_load_secure_api_keys", lambda path: {})
monkeypatch.delenv("DATA_GO_KR_API_KEY", raising=False)
monkeypatch.delenv("VWORLD_API_KEY", raising=False)
monkeypatch.delenv("GEMINI_API_KEY", raising=False)
missing = config.check_api_keys()
assert "GEMINI_API_KEY" in missing

122
tests/test_gemini_image.py Normal file
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@@ -0,0 +1,122 @@
from __future__ import annotations
from unittest.mock import MagicMock
import pytest
from PIL import Image as PILImage
def test_service_defaults_to_nano_banana_pro_model() -> None:
from civilplan_mcp.services.gemini_image import GeminiImageService
service = GeminiImageService(api_key="test-key", client=MagicMock())
assert service.api_key == "test-key"
assert service.model == "gemini-3-pro-image-preview"
def test_service_requires_sdk_or_client() -> None:
from civilplan_mcp.services import gemini_image
from civilplan_mcp.services.gemini_image import GeminiImageService
original_genai = gemini_image.genai
gemini_image.genai = None
try:
with pytest.raises(RuntimeError):
GeminiImageService(api_key="test-key")
finally:
gemini_image.genai = original_genai
def test_generate_image_calls_api_and_saves_output(tmp_path) -> None:
from civilplan_mcp.services.gemini_image import GeminiImageService
mock_client = MagicMock()
mock_image = MagicMock()
mock_part = MagicMock()
mock_part.inline_data = MagicMock()
mock_part.text = None
mock_part.as_image.return_value = mock_image
mock_client.models.generate_content.return_value = MagicMock(parts=[mock_part])
service = GeminiImageService(api_key="test-key", client=mock_client)
output_path = tmp_path / "generated.png"
result = service.generate_image(
prompt="Generate a bird's-eye render of a Korean road project.",
output_path=str(output_path),
aspect_ratio="16:9",
image_size="2K",
)
assert result["status"] == "success"
assert result["path"] == str(output_path)
mock_image.save.assert_called_once_with(str(output_path))
call_kwargs = mock_client.models.generate_content.call_args.kwargs
assert call_kwargs["model"] == "gemini-3-pro-image-preview"
assert call_kwargs["contents"] == ["Generate a bird's-eye render of a Korean road project."]
assert call_kwargs["config"] is not None
def test_generate_image_with_reference_includes_image_content(tmp_path) -> None:
from civilplan_mcp.services.gemini_image import GeminiImageService
reference_path = tmp_path / "reference.png"
PILImage.new("RGB", (8, 8), "gray").save(reference_path)
mock_client = MagicMock()
mock_image = MagicMock()
mock_part = MagicMock()
mock_part.inline_data = MagicMock()
mock_part.text = None
mock_part.as_image.return_value = mock_image
mock_client.models.generate_content.return_value = MagicMock(parts=[mock_part])
service = GeminiImageService(api_key="test-key", client=mock_client)
output_path = tmp_path / "generated.png"
result = service.generate_image(
prompt="Generate a road perspective render.",
output_path=str(output_path),
reference_image_path=str(reference_path),
)
assert result["status"] == "success"
contents = mock_client.models.generate_content.call_args.kwargs["contents"]
assert len(contents) == 2
assert contents[0] == "Generate a road perspective render."
def test_generate_image_returns_error_on_api_failure(tmp_path) -> None:
from civilplan_mcp.services.gemini_image import GeminiImageService
mock_client = MagicMock()
mock_client.models.generate_content.side_effect = RuntimeError("API error")
service = GeminiImageService(api_key="test-key", client=mock_client)
result = service.generate_image(
prompt="Generate a river project render.",
output_path=str(tmp_path / "generated.png"),
)
assert result["status"] == "error"
assert "API error" in result["error"]
def test_generate_image_returns_error_when_response_has_no_image(tmp_path) -> None:
from civilplan_mcp.services.gemini_image import GeminiImageService
mock_client = MagicMock()
mock_part = MagicMock()
mock_part.inline_data = None
mock_part.text = "No image available."
mock_client.models.generate_content.return_value = MagicMock(parts=[mock_part])
service = GeminiImageService(api_key="test-key", client=mock_client)
result = service.generate_image(
prompt="Generate a building render.",
output_path=str(tmp_path / "generated.png"),
)
assert result["status"] == "error"
assert "no image" in result["error"].lower()

View File

@@ -11,14 +11,15 @@ def test_build_server_config_defaults() -> None:
assert config["path"] == "/mcp" assert config["path"] == "/mcp"
def test_server_registers_all_19_tools() -> None: def test_server_registers_all_20_tools() -> None:
app = build_mcp() app = build_mcp()
tools = asyncio.run(app.list_tools()) tools = asyncio.run(app.list_tools())
names = {tool.name for tool in tools} names = {tool.name for tool in tools}
assert len(names) == 19 assert len(names) == 20
assert "civilplan_parse_project" in names assert "civilplan_parse_project" in names
assert "civilplan_generate_dxf_drawing" in names assert "civilplan_generate_dxf_drawing" in names
assert "civilplan_generate_birdseye_view" in names
def test_read_tools_have_read_only_hint() -> None: def test_read_tools_have_read_only_hint() -> None:

26
tests/test_setup_keys.py Normal file
View File

@@ -0,0 +1,26 @@
from __future__ import annotations
from pathlib import Path
from civilplan_mcp import setup_keys
def test_main_prompts_and_saves_gemini_api_key(monkeypatch, tmp_path: Path) -> None:
prompted_values = iter(["data-key", "vworld-key", "gemini-key"])
saved_payload: dict[str, str] = {}
monkeypatch.setattr(setup_keys, "_prompt_value", lambda name, current="": next(prompted_values))
monkeypatch.setattr(
setup_keys,
"save_api_keys",
lambda payload: saved_payload.update(payload) or tmp_path / "api-keys.dpapi.json",
)
exit_code = setup_keys.main([])
assert exit_code == 0
assert saved_payload == {
"DATA_GO_KR_API_KEY": "data-key",
"VWORLD_API_KEY": "vworld-key",
"GEMINI_API_KEY": "gemini-key",
}

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@@ -3,7 +3,7 @@ from civilplan_mcp.config import Settings, get_settings
def test_package_version_present() -> None: def test_package_version_present() -> None:
assert __version__ == "1.0.0" assert __version__ == "2.0.0"
def test_settings_have_expected_defaults() -> None: def test_settings_have_expected_defaults() -> None: