AI assistants are most useful when they can access internal data and tools via MCP. MCP gateways help to make that process easier by managing connections and authentication for your organization. This article compares IBM's ContextForge open source project to MintMCP - a gateway built specifically for enterprises using MCP internally.

Key Takeaways​

• Fundamental difference: IBM ContextForge is open-source MCP gateway, MintMCP is managed enterprise service
• Maturity gap: ContextForge is in alpha/beta with no official IBM support, MintMCP is production SaaS
• Authentication model: ContextForge has admin-level shared auth, MintMCP provides per-user OAuth
• Best fit: ContextForge for platform teams wanting to customize, MintMCP for internal enterprise deployment

AI assistants are most useful when they can access internal data and tools via MCP. MCP gateways help to make that process easier by managing connections and authentication for your organization. This article compares LiteLLM's MCP offering as part of their LLM proxy to the MintMCP - a gateway built specifically for enterprises using MCP internally.

Key Takeaways​

• Fundamental difference: LiteLLM is an LLM proxy with MCP added, MintMCP is purpose-built for MCP
• Custom MCP servers: With LiteLLM you deploy and manage them yourself, MintMCP runs them in their cloud
• Authentication: LiteLLM uses pass-through auth (you manage credentials), MintMCP provides managed OAuth
• Best fit: LiteLLM for developer teams building products, MintMCP for internal enterprise deployments

Deploying MCP servers in production requires choosing the right approach for your use case. This guide covers the four main deployment patterns and helps you select between Cloudflare Workers, Vercel Functions, and MCP gateways based on your specific requirements.

AI agents need access to tools, data sources, and APIs. The Model Context Protocol (MCP) standardizes these interactions, but managing multiple MCP servers introduces complexity. MCP gateways address the technical problems that emerge in production deployments.

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​

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:

1. Exposes each MCP tool as a separate REST endpoint
2. Translates incoming REST requests to MCP tools/call invocations
3. 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.

Custom actions let ChatGPT call your HTTP endpoints. They are ChatGPT's version of tool access, similar to Model Context Protocol (MCP). However, there are some important things to note while building for ChatGPT custom actions. We share what we learned here to help you design actions that behave well in chat.

When you first discover the Model Context Protocol (MCP), it can feel a bit like magic: suddenly your AI assistant can read from a database, update a CRM record, or spin up cloud resources - all through a single, standard interface. But as soon as you try to move beyond a demo, you'll run into practical questions: How do you secure these tool calls? Who keeps track of rate limits and audit logs? Where do you plug in observability? That's where an MCP gateway comes in. Think of it as the operations and security layer that makes MCP usable in production - similar to how an API gateway fronts traditional REST or gRPC services.

We're excited to launch this blog where we'll be sharing updates, tutorials, and insights about the Model Context Protocol (MCP) and how MintMCP Gateway accelerates AI innovation and deployment at your company.

What's Coming Soon​

In the coming weeks, we'll be sharing:

• Getting Started Guides: Step-by-step tutorials to help you get up and running with MintMCP Gateway
• Best Practices: Tips and patterns for building robust MCP integrations
• Feature Spotlights: Deep dives into specific capabilities and use cases
• Community Highlights: Showcasing interesting projects and integrations from our users
• Technical Deep Dives: Architecture insights and implementation details

Stay tuned for more content as we build out this resource together!

Get Involved​

We'd love to hear from you! If you have topics you'd like us to cover or experiences to share, please reach out.

Happy building! 🚀