How to Use MCP Servers with Custom GPTs
Why Bring MCP into Your GPT Workflow?
Custom GPTs become powerful when they can interact with external tools and services. The Model Context Protocol (MCP) provides a standardized way for servers to expose tools, resources, and prompts that AI assistants can discover and invoke. By connecting MCP servers to Custom GPTs, you can unlock access to a growing ecosystem of MCP-compatible tools without building custom integrations for each one.
The Architecture Challenge
Custom GPTs and MCP servers operate on fundamentally different principles. Custom GPTs interact with tools through individual REST endpoints (/api/weather, /api/search, /api/database), each representing a specific capability. In contrast, MCP servers expose all their tools through a single JSON-RPC method called tools/call, where the tool name is passed as a parameter.
This architectural difference means you can't directly connect an MCP server to a Custom GPT. Instead, you need an HTTP gateway to bridge the gap.
System Architecture
How the Gateway Translates Between Protocols
The HTTP gateway performs protocol translation between REST and MCP. On the Custom GPT side, it exposes individual REST endpoints like /api/weather or /api/database-query. On the MCP side, it consolidates all requests through the single tools/call JSON-RPC method.
This translation is essential, because without it, Custom GPTs have no way to communicate with MCP's unified tool interface.
Tool Discovery Mechanisms
| Custom GPT (OpenAPI) | MCP Server |
|---|---|
| Tools defined statically in OpenAPI spec | Tools discovered dynamically via tools/list method |
| ChatGPT reads spec once when Action is added | AI agents can query available tools at runtime |
| Fixed set of endpoints | Tools can change based on context/permissions |
MCP servers expose a tools/list method that returns available tools and their schemas dynamically. This enables AI clients to discover new capabilities at runtime. Custom GPTs, however, require static OpenAPI specifications defined when the Action is configured.
Putting It All Together
To connect MCP servers to Custom GPTs, deploy an HTTP gateway that:
- Exposes each MCP tool as a separate REST endpoint
- Translates incoming REST requests to MCP
tools/callinvocations - Converts MCP responses back to HTTP format
Once deployed, generate an OpenAPI specification for the gateway's REST endpoints and configure it as a Custom GPT Action. From ChatGPT's perspective, it's interacting with a standard REST API-the underlying MCP protocol remains transparent.