MCP服务器与PayLink集成示例

basic-mcp+agent/
├── README.md                    # This file
├── example_mcp_server/          # The MCP server
│   ├── main.py                  # Server implementation
│   ├── pyproject.toml           # Dependencies
│   ├── .env.example             # Environment variable template
│   └── README.md
└── agent/                       # The AI agent consumer
    ├── src/
    │   └── graph.py             # LangGraph agent definition
    ├── notebooks/
    │   └── use_monitized_mcp.ipynb  # Interactive example
    ├── langgraph.json           # LangGraph configuration
    ├── pyproject.toml           # Dependencies
    ├── .env.example             # Environment variable template
    └── README.md

# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv sync

# Run the server
uv run main.py

# Navigate to the agent directory
cd agent

# Copy the environment template
cp .env.example .env

# Edit .env and replace the values with your custom values:
# - WALLET_CONNECTION_STRING (generate on PayLink platform)
# - OPENAI_API_KEY (your OpenAI API key)

# Create and activate virtual environment
uv venv
source .venv/bin/activate

# Install dependencies
uv sync

# Run the LangGraph development server
langgraph dev

User: "What is 15 plus 27?"

1. Agent receives the query
2. LLM decides to use the "add" tool
3. PayLinkTools sends request to MCP server
   - Includes wallet credentials in headers
4. MCP server receives the request
5. Tool executes: 15 + 27 = 42
6. Result returned to agent
7. Agent responds: "15 plus 27 equals 42"

@app.list_tools()
async def list_tools() -> list[types.Tool]:
    return [
        # ... existing tools
        types.Tool(
            name="multiply",
            description="Multiply two integers",
            inputSchema={
                "type": "object",
                "properties": {
                    "a": {"type": "number"},
                    "b": {"type": "number"},
                },
                "required": ["a", "b"],
            },
        ),
    ]

@app.call_tool()
async def call_tool(tool_name: str, arguments: dict[str, Any]) -> list[TextContent]:
    # ... existing logic
    elif tool_name == "multiply":
        result = arguments["a"] * arguments["b"]
        return [types.TextContent(type="text", text=str(result))]