mcp.py

import os
import asyncio
from datetime import datetime, timedelta, timezone
from mcp_use import MCPClient

async def main():
    client = MCPClient.from_config_file("multi_mcp_config.json")

    try:
        # compute cutoff ISO timestamp
        cutoff = datetime.now(timezone.utc) - timedelta(minutes=5)
        cutoff_iso = cutoff.isoformat()  # e.g. '2025-07-30T14:25:00+00:00'

        # 1. fetch messages after cutoff
        recent_msgs = await client.call_tool(
            server_name="discord",
            tool_name="read_messages",
            params={
                "channel_id": os.getenv("CHANNEL_ID"),
                "after": cutoff_iso,    # only messages in last 5m
                "limit": 1000
            }
        )

        # 2. filter by USER_ID in Python
        user_id = os.getenv("USER_ID")
        user_msgs = [
            m for m in recent_msgs
            if m.get("author", {}).get("id") == user_id
        ]

        # 3. upsert into Postgres
        for m in user_msgs:
            content = m["content"].replace("'", "''")
            sql = (
                "INSERT INTO user_messages (id, author_id, content, ts) "
                f"VALUES ('{m['id']}', '{user_id}', '{content}', '{m['timestamp']}') "
                "ON CONFLICT DO NOTHING"
            )
            await client.call_tool(
                server_name="postgres",
                tool_name="execute_query",
                params={"sql": sql}
            )

        # 4. score each category directly
        category_scores = await client.call_tool(
            server_name="openai",
            tool_name="create_chat_completion",
            params={
                "model": "gpt-4",
                "messages": [
                    {
                        "role": "system",
                        "content": (
                            "Based on these user messages, assign scores 1-10 for each category "
                            "(meme, technical, builder, helpful, random) for this user. "
                            "Return JSON with category names as keys and scores as values."
                        )
                    },
                    {"role": "user", "content": str(user_msgs)}
                ]
            }
        )

        print("Category scores:", category_scores)

        # 5. update user scores in postgres
        for category, score in category_scores.items():
            sql = (
                f"INSERT INTO user_category_scores (user_id, category, score, updated_at) "
                f"VALUES ('{user_id}', '{category}', {score}, NOW()) "
                f"ON CONFLICT (user_id, category) DO UPDATE SET "
                f"score = {score}, updated_at = NOW()"
            )
            await client.call_tool(
                server_name="postgres",
                tool_name="execute_query",
                params={"sql": sql}
            )

    finally:
        await client.close_all_sessions()

if __name__ == "__main__":
    asyncio.run(main())

multi_mcp_config.json

{
  "mcpServers": {
    "discord": {
      "command": "mcp-discord",
      "args": [],
      "env": {
        "DISCORD_TOKEN": "$DISCORD_TOKEN"
      }
    },
    "postgres": {
      "command": "postgres-mcp",
      "args": [
        "--access-mode=unrestricted"
      ],
      "env": {
        "DATABASE_URI": "$DATABASE_URL"
      }
    },
    "openai": {
      "command": "python",
      "args": [
        "-m",
        "src.mcp_server_openai.server"
      ],
      "env": {
        "PYTHONPATH": "/path/to/mcp-server-openai",
        "OPENAI_API_KEY": "$OPENAI_API_KEY"
      }
    }
  }
}