Migrating existing APIs to MCP

You have a stable of REST APIs powering your product, and now you want AI assistants or large language models (LLMs) to tap into those same services. How do you bridge that gap without rebuilding everything from scratch? Enter Model Context Protocol (MCP) – an open standard that acts as a universal adapter for AI integrations.

By migrating your existing APIs to MCP, you effectively give them a “USB-C port” for AI: a standard interface any MCP-enabled AI agent can plug into. This article provides a strategic overview of why and how to undertake this migration, aimed at developers, AI infrastructure teams, product managers, engineering managers, and decision-makers responsible for AI implementation.

Most MCP tutorials start with toy examples and clean, fresh APIs. Reality check: most of us already have 50+ APIs. Migrating those into usable MCP tools isn’t a copy-paste job — it’s a process. Here’s what worked (and didn’t) when we tried.

1. Start simple, expect breakage

We began with a direct 1:1 mapping of existing API endpoints to MCP tools.

• It technically worked, but performance and usability suffered.
• Tools created context bloat, and models like Claude were inconsistent in selecting the right one.
• As one Redditor put it: *”Naively hooking it up just created context-bloating garbage.”

Lesson:
Direct mapping is tempting, but not sustainable. Like data modeling, MCP tool design requires intention. Focus on how agents will use a tool, not just what your API exposes.

2. Test real use cases, not just endpoints

Instead of wiring up every API, we picked 10 to 20 real-world use cases and tested them against the tools.

• Prompted the model to solve a task.
• Logged which tools it called.
• Observed failure points and blind spots.

Lesson:
Your endpoint list is not your roadmap. Start from the questions users or agents are likely to ask. For instance, Select 10–20 use cases and test them. See what the model calls, what breaks, and iterate from there.

3. Fix what breaks, and things will break

Certain patterns of failure kept showing up, especially early on:

• Too much info → add pagination.
• No bulk fetch → move from get by ID to fetch many.
• Input mismatches → accept strings and integers, handle fuzzy formats.
• Inefficient call chains → merge 3 or 4 tool calls into one composite tool.

Lesson:
Edge cases aren’t theoretical. They show up as soon as agents start using your tools. Be ready to patch and rework. For instance, if you see the AI calling multiple tools in sequence, replace them with one that just gives what it needs.

4. Iterate like you’re building a product

You’re not just writing wrappers. You’re designing a product interface for agents.

• Update tool descriptions for clarity.
• Rerun test use cases after each change.
• Add new test cases to keep evolving coverage.
• Run regression tests across different models (Claude, GPT-4, etc.)

Lesson:
Tool descriptions are your UX for LLMs. Iterate like it’s a user-facing feature.

5. Don’t ignore scale, safety, and context

Once tools started working well, new concerns emerged. One issue was duplicated tools across servers. We solved it by extracting shared tools into modules. Brought in package management overhead, but worth it.

• Load testing mattered. LLMs can fire multiple tool calls rapidly.
• Permissions had to be enforced. Not all tools should be available to every agent or flow.
• Guiding prompts and lightweight context snippets helped tools perform more predictably.

Lesson:
Tool behavior is only part of the job. Stability, security, and contextual guidance matter just as much.

6. Migration is not a moment; it is a motion

There is no one-click MCP migration. Even Microsoft’s Azure DevOps MCP server is just a wrapper around their current API. It is more like layering.

• Start by wrapping existing APIs, even if imperfect.
• Iterate on specific use cases, not entire schemas.
• Gradually introduce new tools that abstract or combine API calls.

Lesson:
Consider this a gradual evolution. Migrate, test, restructure, repeat.

Final takeaways

• MCP is not plug and play. Design still matters. Tools must be intentional, not just mechanically converted.
• The best MCP tools feel like a good data model: boring, deliberate, and clear.
• 1:1 mapping is a trap. It just moves the complexity to your agents.
• The most effective approach:
  • Pick meaningful use cases
  • Test with real agents
  • Adapt tools accordingly
  • Repeat

Your API does not need to be perfect on day one. It just needs to speak agent. That starts with listening to how agents fail.