flowchart LR
    C[MCP 客户端\nClaude/Cursor/Agent] -->|自然语言| MCP[FinSage MCP Server]
    API[FastAPI /api/v1] -->|自然语言| SVC[编排层 services]
    MCP --> SVC
    SVC --> LLM[LLM 层\n解析意图+合成]
    SVC --> DATA[数据层\n行情/财务]
    LLM -. mock / OpenAI兼容 .-> LLMIMPL[(LLMProvider)]
    DATA -. mock / akshare .-> DATAIMPL[(DataProvider)]
    SVC --> RESP[结构化 JSON 响应]

pip install -r requirements.txt
python -m finsage            # 或: finsage-mcp

FINSAGE_MCP_TRANSPORT=sse python -m finsage

{
  "mcpServers": {
    "finsage": {
      "command": "python",
      "args": ["-m", "finsage"],
      "env": {
        "FINSAGE_DATA_PROVIDER": "mock",
        "FINSAGE_LLM_PROVIDER": "mock"
      }
    }
  }
}

npx @modelcontextprotocol/inspector python -m finsage

pip install -r requirements.txt
uvicorn finsage.main:app --reload --port 8000
# 打开 http://localhost:8000/docs

docker compose up --build

pip install ".[real]"        # 安装 akshare + openai
cp .env.example .env
# 编辑 .env:
#   FINSAGE_DATA_PROVIDER=akshare
#   FINSAGE_LLM_PROVIDER=openai
#   FINSAGE_LLM_API_KEY=sk-xxx
python -m finsage            # 或 uvicorn finsage.main:app --port 8000

用户: 分析贵州茅台的毛利率和ROE
→ analyze_stock(query="分析贵州茅台的毛利率和ROE", symbol="600519")

用户: 筛选低估值高ROE的白酒股
→ screen_stocks(query="低估值高ROE的白酒股", top_n=10)

用户: 给我 600519 的公司简报
→ stock_report(symbol="600519", include_risk=true)

用户: 对比贵州茅台和五粮液
→ compare_stocks(symbols=["600519","000858"])

用户: 给我一份贵州茅台的综合研究，顺便和五粮液比一下
→ research_company(symbol="600519", compare_with=["000858"])

# 综合研究（分析 + 简报 + 同业对比，一步到位）
curl -X POST http://localhost:8000/api/v1/research \
  -H 'Content-Type: application/json' \
  -d '{"symbol":"600519","compare_with":["000858"]}'

curl -X POST http://localhost:8000/api/v1/analyze \
  -H 'Content-Type: application/json' \
  -d '{"query":"分析贵州茅台的毛利率和ROE","symbol":"600519"}'

# 对比多标的
curl -X POST http://localhost:8000/api/v1/compare \
  -H 'Content-Type: application/json' \
  -d '{"symbols":["600519","000858"]}'

pip install pytest pytest-asyncio
pytest -q