Chat-MCP
一个用于在对话中调用MCP（Model Context Protocol）工具的Python客户端库
Chat-MCP 提供了一个简洁而强大的接口，让您能够轻松地将MCP服务器集成到AI agent项目中。通过两个简单的api，并配置好.env文件，就可以在开发agent和chat助手时高效的调用mcp服务。作为agentic项目的底层脚手架，分享大家使用。
核心特性
- 简洁的API设计: 二个主要接口覆盖所有使用场景，提高agent应用开发效率
- 优化提示词: 提高tools调用的成功率，持续优化中
- 异步架构: 完全基于asyncio的高性能处理
- 智能工具管理: 自动工具收集、过滤和并行执行
- 多模型支持: 通过LiteLLM支持多种LLM提供商，支持本地模型配置
关键技术栈
- MCP Python SDK: Model Context Protocol的官方Python实现
- LiteLLM: 统一的LLM API接口库，支持100+模型
- Pydantic: 数据验证和设置管理框架
项目结构
chat-mcp/
├── docs/ # 详细的开发文档和规范
├── src/chat_mcp/ # 主要源代码包
│ ├── tests/ # 测试套件
│ ├── __init__.py # 公共API导出
│ ├── easy_chat.py # 简化API接口
│ ├── ai_provider.py # LLM提供商集成
│ ├── mcp_service.py # MCP服务器管理
│ ├── mcp_chat_handler.py # 聊天处理逻辑
│ ├── ipc_handler.py # 进程间通信处理
│ └── mcp_types.py # 数据类型定义
├── temp_arxiv_storage/ # ArXiv演示数据存储
├── pyproject.toml # uv项目配置
├── uv.lock # 依赖锁定文件
└── main.py # 演示入口
核心API
MCPChatTool - 主要接口
from chat_mcp import MCPChatTool
# 创建聊天工具实例
chat_tool = MCPChatTool()
# 启动MCP服务器，这里使用arxiv-mcp-server作为演示样例，可以添加多个不同的mcp server，只需要多次调用
server = await chat_tool.start_mcp_server(
server_id="arxiv",
name="ArXiv Research",
command="uv",
args=["tool", "run", "arxiv-mcp-server"]
)
# 进行对话
result = await chat_tool.chat_with_mcp(
user_message="搜索关于机器学习的最新论文",
enabled_server_ids=["arxiv"] # 这里可以不设置enabled_server_ids,默认所有server都会被添加
)
安装
使用 uv (推荐):
uv add chat-mcp
或使用 pip:
pip install chat-mcp
快速开始
- 配置环境变量 (创建
.env文件):
LLM配置：使用litellm规范，可以自行配置，支持大部分模型包括自定义大模型
OPENAI_API_KEY=your_openai_api_key
OPENAI_API_BASE=https://api.openai.com/v1
LLM_MODEL=gpt-3.5-turbo
如果是vllm本地模型可以如下配置
MODEL_NAME="openai//{VLLM_MODEL_NAME(PATH)}"
OPENAI_API_KEY="xxxxxxxxxxxxxxxxxxxxxxxxxxx"
OPENAI_API_BASE="http://xxx.xxx.xxx.xxx:xxxx/v1"
其他模型都已自行尝试配置，具体参考litellm支持的模型
- 基础使用:
import asyncio
from chat_mcp import MCPChatTool
async def main():
# 创建聊天工具
chat_tool = MCPChatTool()
# 添加ArXiv服务器
await chat_tool.start_mcp_server(
server_id="arxiv",
name="ArXiv Research",
command="uv",
args=["tool", "run", "arxiv-mcp-server"]
)
# 开始对话
result = await chat_tool.chat_with_mcp(
user_message="帮我搜索最新的深度学习论文",
system_prompt="你是一个研究助手，专门帮助用户搜索和分析学术论文。"
)
print(f"AI回答: {result['content']}")
print(f"工具调用: {len(result['tool_calls'])} 次")
# 运行
asyncio.run(main())
许可证
MIT License - 详见 LICENSE 文件
Chat-MCP: 让AI对话更智能，让工具调用更简单
1 maintainer
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