Genome MCP is an intelligent genomic data server providing high-quality gene information, homologous gene analysis, evolutionary research, and pathway enrichment through the MCP protocol, powered by authoritative databases (NCBI Gene, Ensembl, KEGG).
Core Capabilities:
Gene & Protein Data - Smart retrieval with automatic query type recognition (gene symbols, IDs, protein details, chromosomal regions like chr17:7565097-7590856), batch processing with parallel execution, and multiple output formats (simple, detailed, raw)
Intelligent Search - Natural language semantic search with context-awareness (genomics, proteomics, pathway), smart result ranking and filtering
Evolutionary & Comparative Analysis - Cross-species homologous gene queries (253+ species via Ensembl), phylogenetic profiling, conservation scoring, and domain information integration
KEGG Pathway Enrichment - Statistical pathway over-representation analysis with FDR correction, multi-organism support (human, mouse, rat, etc.), and customizable significance thresholds
Advanced Query Features - Complex batch queries with custom strategies (parallel/sequential), flexible filtering, result pagination, and optimized execution paths
Integration & Performance - Multiple transport protocols (STDIO, HTTP, SSE), asynchronous architecture for high-performance processing, compatible with MCP clients (Claude Desktop, Continue.dev, Cursor, Cline, Windsurf), and scientific reliability with no simulated data
Distributed as a Python package through PyPI, providing intelligent genome data services with gene information queries, homolog analysis, and evolutionary research capabilities.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@genome-mcpshow me the homologous genes for TP53 in mice"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Genome MCP
🧬 智能基因组数据服务器 - 通过MCP协议提供高质量的基因信息查询、同源基因分析和进化研究功能。可在 Glama MCP平台 发现和快速配置。
1. 🚀 核心特性
🧬 基因信息查询: 基于NCBI Gene数据库的准确基因信息
🔄 同源基因分析: 基于Ensembl API的跨物种同源基因查询（253+ TP53同源基因）
🧬 进化分析: 系统发育关系构建和保守性分析
🔍 语义搜索: 理解查询意图的智能搜索功能
📊 批量处理: 优化的并发查询，支持大规模数据分析
🌐 多传输模式: 支持STDIO、HTTP、SSE传输协议
⚡ 异步架构: 高性能异步处理架构
🔬 科学可靠: 基于权威数据库，无模拟数据，完全科学可信
2. 安装
推荐使用现代化的 uv 包管理器以获得更快的安装速度：
# 使用uvx直接运行（推荐）
uvx genome-mcp
# 或添加到项目
uv add genome-mcp传统方式安装：
pip install genome-mcp3. 🛠️ MCP 接入配置
3.1 Claude Desktop
编辑配置文件：
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
推荐使用 uvx 运行:
{
"mcpServers": {
"genome-mcp": {
"command": "uvx",
"args": ["genome-mcp"],
"env": {}
}
}
}或使用传统方式:
{
"mcpServers": {
"genome-mcp": {
"command": "python",
"args": ["-m", "genome_mcp"],
"env": {}
}
}
}或使用 uv run:
{
"mcpServers": {
"genome-mcp": {
"command": "uv",
"args": ["run", "-m", "genome_mcp"],
"env": {}
}
}
}3.2 Continue.dev
在 VS Code 的 Continue.dev 扩展配置中:
{
"mcpServers": {
"genome-mcp": {
"command": "uvx",
"args": ["genome-mcp"]
}
}
}3.3 Cursor (VS Code 扩展)
在 Cursor 设置中添加:
{
"mcpServers": {
"genome-mcp": {
"command": "uvx",
"args": ["genome-mcp"],
"env": {
"GENOME_MCP_LOG_LEVEL": "info"
}
}
}
}3.4 Cline (Claude for VS Code)
在 Cline 设置文件中:
{
"mcpServers": {
"genome-mcp": {
"command": "uvx",
"args": ["genome-mcp"],
"timeout": 30000
}
}
}3.5 其他支持 MCP 的客户端
Windsurf: 使用与 Claude Desktop 相同的配置格式
OpenHands: 在 config.json 中添加服务器配置
Custom MCP Client: 参考下面的 Python 示例
3.6 自定义 MCP 客户端
使用 stdio 传输:
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)4. 🔧 API 功能
4.1 可用工具
get_data - 智能数据获取
支持基因符号、ID、区域搜索、同源基因查询
自动类型识别和查询优化
批量查询支持
advanced_query - 高级批量查询
复杂查询条件组合
批量处理优化
自定义输出格式
smart_search - 语义搜索
自然语言查询理解
智能结果排序
上下文感知搜索
kegg_pathway_enrichment_tool - KEGG通路富集分析 🆕
基因列表在KEGG通路中的富集分析
超几何分布检验计算统计显著性
FDR多重检验校正
支持人类、小鼠、大鼠等多种模式生物
4.2 使用示例
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())5. 📋 响应格式
所有API响应都遵循统一的JSON格式，包含 success、data 和 query_info 字段。
示例响应：
{
"success": true,
"data": {
"gene_info": {
"uid": "7157",
"name": "TP53",
"description": "tumor protein p53"
}
},
"query_info": {
"query": "TP53",
"query_type": "gene"
}
}6. 💻 命令行使用
# 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 --help7. 📋 更新日志
详细的版本更新记录请查看 CHANGELOG.md
8. 📚 依赖
详细的依赖信息和版本要求请查看 pyproject.toml
Python 版本要求：>= 3.11
9. 🏗️ 开发
git clone https://github.com/gqy20/genome-mcp
cd genome-mcp
pip install -e ".[dev]"
make test
make lint9.1 开发命令
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10. 📄 许可证
本项目采用 MIT License 开源许可证。
© 2025 gqy20
11. 🤝 贡献
欢迎提交 Issue 和 Pull Request！
12. 📞 支持
Genome MCP - 让基因组数据访问更简单、更智能！