mcp-agent/
├── agents.py              # Main application (FastAPI + LangGraph)
├── mcp_server.py          # MCP server with agricultural tools
├── config.json            # MCP server configuration
├── .env                   # Environment variables (API keys)
├── requirements.txt       # Python dependencies
├── README.md              # This file
├── ARCHITECTURE.md        # System architecture documentation
└── DATAFLOW.md           # Complete data flow explanation

pip install -r requirements.txt

GOOGLE_API_KEY=your_google_api_key_here

{
  "mcpServers": {
    "agricultural-server": {
      "command": "python",
      "args": ["mcp_server.py"],
      "env": {
        "PYTHONIOENCODING": "utf-8"
      }
    }
  }
}

{
  "command": "D:\\Python\\python.exe",
  "args": ["D:\\projects\\mcp-agent\\mcp_server.py"]
}

python agents.py

curl -X POST "http://localhost:8000/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the weather in Tokyo?"}'

# Weather query → Uses get_current_weather
"What's the weather in Paris?"

# Agriculture query → Uses get_pesticide_seed_info  
"Tell me about organic farming techniques"

# Content query → Uses get_placeholder_posts
"Show me 5 interesting articles"

# Multi-step reasoning → Uses multiple tools
"What's the weather in Mumbai and what crops grow best there?"

{
  "message": "Your query here"
}

{
  "response": "Agent's answer",
  "intermediate_steps": ["Tool used: get_current_weather"],
  "error": null
}

@mcp_server.list_tools()
async def list_tools() -> list[Tool]:
    return [
        # ... existing tools ...
        Tool(
            name="my_new_tool",
            description="What this tool does",
            inputSchema={
                "type": "object",
                "properties": {
                    "param": {"type": "string"}
                },
                "required": ["param"]
            }
        )
    ]