158 lines
5.5 KiB
Python
158 lines
5.5 KiB
Python
#!/usr/bin/env python3
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"""
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测试MCP服务器的脚本
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"""
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import asyncio
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import json
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import subprocess
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import sys
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from typing import Dict, Any, List
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async def test_mcp_server(server_name: str, command: List[str], env: Dict[str, str] = None):
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"""测试MCP服务器"""
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print(f"\n=== 测试 {server_name} 服务器 ===")
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# 设置环境变量
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process_env = {}
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if env:
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process_env.update(env)
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try:
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# 启动服务器进程
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process = await asyncio.create_subprocess_exec(
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*command,
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stdin=asyncio.subprocess.PIPE,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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env=process_env
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)
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# 初始化请求
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init_request = {
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"jsonrpc": "2.0",
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"id": 1,
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"method": "initialize",
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"params": {
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"protocolVersion": "2024-11-05",
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"capabilities": {
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"tools": {}
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}
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}
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}
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# 发送初始化请求
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process.stdin.write((json.dumps(init_request) + "\n").encode())
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await process.stdin.drain()
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# 读取初始化响应
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init_response = await process.stdout.readline()
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if init_response:
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try:
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init_data = json.loads(init_response.decode())
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print(f"初始化响应: {init_data}")
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except json.JSONDecodeError:
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print(f"初始化响应解析失败: {init_response}")
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# 获取工具列表
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tools_request = {
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"jsonrpc": "2.0",
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"id": 2,
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"method": "tools/list"
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}
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# 发送工具列表请求
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process.stdin.write((json.dumps(tools_request) + "\n").encode())
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await process.stdin.drain()
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# 读取工具列表响应
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tools_response = await process.stdout.readline()
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if tools_response:
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try:
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tools_data = json.loads(tools_response.decode())
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print(f"工具列表: {json.dumps(tools_data, indent=2, ensure_ascii=False)}")
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# 如果有搜索工具，测试搜索功能
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if "result" in tools_data and "tools" in tools_data["result"]:
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for tool in tools_data["result"]["tools"]:
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tool_name = tool.get("name")
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if tool_name and ("search" in tool_name or "document" in tool_name):
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print(f"\n测试工具: {tool_name}")
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# 测试搜索工具
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search_request = {
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"jsonrpc": "2.0",
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"id": 3,
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"method": "tools/call",
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"params": {
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"name": tool_name,
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"arguments": {
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"query": "测试查询",
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"limit": 3
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}
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}
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}
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# 发送搜索请求
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process.stdin.write((json.dumps(search_request) + "\n").encode())
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await process.stdin.drain()
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# 读取搜索响应
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search_response = await process.stdout.readline()
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if search_response:
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try:
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search_data = json.loads(search_response.decode())
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print(f"搜索结果: {json.dumps(search_data, indent=2, ensure_ascii=False)}")
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except json.JSONDecodeError:
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print(f"搜索响应解析失败: {search_response}")
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break
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except json.JSONDecodeError:
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print(f"工具列表响应解析失败: {tools_response}")
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# 关闭进程
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process.stdin.close()
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await process.wait()
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except Exception as e:
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print(f"测试 {server_name} 服务器时出错: {e}")
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async def main():
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"""主函数"""
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print("开始测试MCP服务器...")
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# 测试context7服务器
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await test_mcp_server(
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"context7",
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["npx", "-y", "@upstash/context7-mcp"],
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{"DEFAULT_MINIMUM_TOKENS": ""}
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)
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# 测试qdrant服务器
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await test_mcp_server(
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"qdrant",
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["ssh", "ben@dev1", "cd /home/ben/qdrant && source venv/bin/activate && python qdrant_mcp_server.py"],
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{
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"QDRANT_URL": "http://dev1:6333",
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"QDRANT_API_KEY": "313131",
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"COLLECTION_NAME": "mcp",
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"EMBEDDING_MODEL": "bge-m3"
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}
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)
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# 测试qdrant-ollama服务器
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await test_mcp_server(
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"qdrant-ollama",
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["ssh", "ben@dev1", "cd /home/ben/qdrant && source venv/bin/activate && ./start_mcp_server.sh"],
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{
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"QDRANT_URL": "http://dev1:6333",
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"QDRANT_API_KEY": "313131",
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"COLLECTION_NAME": "ollama_mcp",
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"OLLAMA_MODEL": "nomic-embed-text",
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"OLLAMA_URL": "http://dev1:11434"
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}
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)
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print("\n所有测试完成。")
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if __name__ == "__main__":
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asyncio.run(main()) |