# 使用uvx直接运行（推荐）
uvx genome-mcp

# 或添加到项目
uv add genome-mcp

pip install genome-mcp

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uvx",
      "args": ["genome-mcp"],
      "env": {}
    }
  }
}

{
  "mcpServers": {
    "genome-mcp": {
      "command": "python",
      "args": ["-m", "genome_mcp"],
      "env": {}
    }
  }
}

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uv",
      "args": ["run", "-m", "genome_mcp"],
      "env": {}
    }
  }
}

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uvx",
      "args": ["genome-mcp"]
    }
  }
}

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uvx",
      "args": ["genome-mcp"],
      "env": {
        "GENOME_MCP_LOG_LEVEL": "info"
      }
    }
  }
}

{
  "mcpServers": {
    "genome-mcp": {
      "command": "uvx",
      "args": ["genome-mcp"],
      "timeout": 30000
    }
  }
}

import subprocess
import json

# 启动 MCP 服务器
process = subprocess.Popen(
    ["python", "-m", "genome_mcp"],
    stdin=subprocess.PIPE,
    stdout=subprocess.PIPE,
    text=True
)

# 发送初始化消息
init_message = {
    "jsonrpc": "2.0",
    "id": 1,
    "method": "initialize",
    "params": {
        "protocolVersion": "2024-11-05",
        "capabilities": {},
        "clientInfo": {"name": "test-client", "version": "1.0.0"}
    }
}

process.stdin.write(json.dumps(init_message) + "\n")
response = process.stdout.readline()
print("Server response:", response)

import asyncio
from genome_mcp import get_data, advanced_query, smart_search

async def main():
    # 获取基因信息
    gene_info = await get_data("TP53")
    print("Gene info:", gene_info)

    # 区域搜索
    region_data = await get_data("chr17:7565097-7590856", query_type="region")
    print("Region data:", region_data)

    # 批量查询
    batch_results = await get_data(["TP53", "BRCA1", "EGFR"], query_type="gene")
    print("Batch results:", batch_results)

    # 语义搜索
    search_results = await smart_search("tumor suppressor genes involved in cancer")
    print("Search results:", search_results)

    # 高级查询
    advanced_results = await advanced_query(
        query="cancer genes",
        query_type="search",
        database="gene",
        max_results=20
    )
    print("Advanced results:", advanced_results)

    # KEGG通路富集分析
    kegg_results = await kegg_pathway_enrichment_tool(
        gene_list=["7157", "672", "675"],  # TP53, BRCA1, BRCA2的Entrez ID
        organism="hsa",
        pvalue_threshold=0.05,
        min_gene_count=2
    )
    print("KEGG enrichment results:", kegg_results)

asyncio.run(main())

{
  "success": true,
  "data": {
    "gene_info": {
      "uid": "7157",
      "name": "TP53",
      "description": "tumor protein p53"
    }
  },
  "query_info": {
    "query": "TP53",
    "query_type": "gene"
  }
}

# 直接运行（推荐）
uvx genome-mcp

# 开发模式运行
uv run -m genome_mcp

# HTTP 服务器模式
uv run -m genome_mcp --port 8080

# 查看帮助
uv run -m genome_mcp --help

git clone https://github.com/gqy20/genome-mcp
cd genome-mcp
pip install -e ".[dev]"
make test
make lint

make install    # 安装开发依赖
make format     # 格式化代码
make lint       # 代码质量检查
make test       # 运行测试
make check      # 完整检查
make build      # 构建包