python-mcp-server-client

Name: python-mcp-server-client
Availability: InStock
Author: Gobin

支持查询主流agent框架技术文档的MCP server（支持stdio和sse两种传输协议）, 支持 langchain、llama-index、autogen、agno、openai-agents-sdk、mcp-doc、camel-ai 和 crew-ai

GitHub

GitHub Stars

135

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Issues

Installation

Difficulty

Intermediate

Estimated Time

10-20 minutes

Requirements

Python 3.7以上

UVの最新バージョン

Installation

Prerequisites

Python: 3.7 or higher

UV: Latest version

Installation Steps

1. Install UV Package

MacOS/Linux:

bash
curl -LsSf https://astral.sh/uv/install.sh | sh

Windows:

powershell
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Initialize Project

bash
Create project directory
uv init mcp-server
cd mcp-server

Create and activate virtual environment
uv venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate

Install dependencies
uv add "mcp[cli]" httpx

Create server implementation file
touch main.py

Troubleshooting

Issue: Server won't start Solution: Check Python version and reinstall dependencies.

Configuration

Basic Configuration

Server Setup

Edit the main.py file to implement the MCP server. Here is a basic configuration example:

python
import json
import os
import httpx
from mcp import tool

async def search_web(query: str) -> dict | None:
    payload = json.dumps({"q": query, "num": 3})
    headers = {
        "X-API-KEY": os.getenv("SERPER_API_KEY"),
        "Content-Type": "application/json",
    }
    async with httpx.AsyncClient() as client:
        response = await client.post(SERPER_URL, headers=headers, data=payload, timeout=30.0)
        return response.json()

Environment Variables

Set the following environment variables as needed:

bash
export SERPER_API_KEY="your-api-key"

Examples

Basic Usage

Here is a basic usage example for the MCP server:

Programmatic Usage

python
import requests
import json

def call_mcp_tool(tool_name, params):
    response = requests.post(
        'http://localhost:3000/mcp/call',
        json={
            'tool': tool_name,
            'parameters': params
        }
    )
    return response.json()

Usage example
result = call_mcp_tool('analyze', {
    'input': 'sample data',
    'options': {'format': 'json'}
})