如何使用模型上下文协议构建自定义工具 原创

1 # Using uvx (recommended)
2 uvx create-mcp-server
3
4 # Or using pip
5 pip install create-mcp-server
6 create-mcp-server
这将创建一个基本的项目结构：
Plain Text 
1 my-server/
2 ├── README.md
3 ├── pyproject.toml
4 └── src/
5    └── my_server/
6        ├── __init__.py
7        ├── __main__.py
8          └── server.py

1 import asyncio
2 from mcp.server.models import InitializationOptions
3 import mcp.types as types
4 from mcp.server import NotificationOptions, Server
5 import mcp.server.stdio
6 import arxiv
7
8 server = Server("mcp-scholarly")
9 client = arxiv.Client()
10
11 @server.list_tools()
12 async def handle_list_tools() -> list[types.Tool]:
13    """
14    List available tools.
15   Each tool specifies its arguments using JSON Schema validation.
16    """
17    return [
18        types.Tool(
19            name="search-arxiv",
20            description="Search arxiv for articles related to the given keyword.",
21            inputSchema={
22                "type": "object",
23                "properties": {
24                    "keyword": {"type": "string"},
25                },
26                "required": ["keyword"],
27            },
28        )
29    ]
30
31 @server.call_tool()
32 async def handle_call_tool(
33        name: str, arguments: dict | None
34 ) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
35    """
36    Handle tool execution requests.
37    Tools can modify server state and notify clients of changes.
38    """
39    if name != "search-arxiv":
40        raise ValueError(f"Unknown tool: {name}")
41    
42    if not arguments:
43        raise ValueError("Missing arguments")
44        
45    keyword = arguments.get("keyword")
46    if not keyword:
47        raise ValueError("Missing keyword")
48
49    # Search arXiv papers
50    search = arxiv.Search(
51        query=keyword, 
52        max_results=10, 
53        sort_by=arxiv.SortCriterion.SubmittedDate
54    )
55    results = client.results(search)
56    
57    # Format results
58    formatted_results = []
59    for result in results:
60        article_data = "\n".join([
61            f"Title: {result.title}",
62            f"Summary: {result.summary}",
63            f"Links: {'||'.join([link.href for link in result.links])}",
64            f"PDF URL: {result.pdf_url}",
65        ])
66        formatted_results.append(article_data)
67
68    return [
69        types.TextContent(
70            type="text",
71            text=f"Search articles for {keyword}:\n"
72                 + "\n\n\n".join(formatted_results)
73        ),
74    ]

lain Text 
1 npx @modelcontextprotocol/inspector uv --directory /your/project/path run your-server-name

1 {
2  "mcpServers": {
3    "mcp-scholarly": {
4      "command": "uv",
5      "args": [
6        "--directory",
7        "/path/to/your/mcp-scholarly",
8        "run",
9        "mcp-scholarly"
10      ]
11    }
12  }
13 }

1 {
2  "mcpServers": {
3    "mcp-scholarly": {
4      "command": "uvx",
5      "args": [
6        "mcp-scholarly"
7      ]
8    }
9  }
10 }