ChatGPT 🤝 FastMCP

ChatGPT supports MCP servers through remote HTTP connections in two modes: Chat mode for interactive conversations and Deep Research mode for comprehensive information retrieval.

Build a Server

First, let's create a simple FastMCP server:

from fastmcp import FastMCP
import random

mcp = FastMCP("Demo Server")

@mcp.tool
def roll_dice(sides: int = 6) -> int:
    """Roll a dice with the specified number of sides."""
    return random.randint(1, sides)

if __name__ == "__main__":
    mcp.run(transport="http", port=8000)

Deploy Your Server

Your server must be accessible from the internet. For development, use ngrok:

ngrok http 8000

Note your public URL (e.g., https://abc123.ngrok.io) for the next steps.

Chat Mode

Chat mode lets you use MCP tools directly in ChatGPT conversations. See OpenAI's Developer Mode guide for the latest requirements.

Add to ChatGPT

1. Enable Developer Mode

1. Open ChatGPT and go to Settings → Connectors
2. Under Advanced, toggle Developer Mode to enabled

2. Create Connector

1. In Settings → Connectors, click Create
2. Enter:
   • Name: Your server name
   • Server URL: https://your-server.ngrok.io/mcp/
3. Check I trust this provider
4. Add authentication if needed
5. Click Create

3. Use in Chat

1. Start a new chat
2. Click the + button → More → Developer Mode
3. Enable your MCP server connector (required - the connector must be explicitly added to each chat)
4. Now you can use your tools:

Example usage:

• "Roll a 20-sided dice"
• "Roll dice" (uses default 6 sides)

Skip Confirmations

Use annotations={"readOnlyHint": True} to skip confirmation prompts for read-only tools:

@mcp.tool(annotations={"readOnlyHint": True})
def get_status() -> str:
    """Check system status."""
    return "All systems operational"

@mcp.tool()  # No annotation - ChatGPT may ask for confirmation
def delete_item(id: str) -> str:
    """Delete an item."""
    return f"Deleted {id}"

Deep Research Mode

Deep Research mode provides systematic information retrieval with citations. See OpenAI's MCP documentation for the latest Deep Research specifications.

Tool Implementation

Deep Research tools must follow this pattern:

@mcp.tool()
def search(query: str) -> dict:
    """
    Search for records matching the query.
    Must return {"ids": [list of string IDs]}
    """
    # Your search logic
    matching_ids = ["id1", "id2", "id3"]
    return {"ids": matching_ids}

@mcp.tool()
def fetch(id: str) -> dict:
    """
    Fetch a complete record by ID.
    Return the full record data for ChatGPT to analyze.
    """
    # Your fetch logic
    return {
        "id": id,
        "title": "Record Title",
        "content": "Full record content...",
        "metadata": {"author": "Jane Doe", "date": "2024"}
    }

Using Deep Research

1. Ensure your server is added to ChatGPT's connectors (same as Chat mode)
2. Start a new chat
3. Click + → Deep Research
4. Select your MCP server as a source
5. Ask research questions

ChatGPT will use your search and fetch tools to find and cite relevant information.