How We Used MCP’s and GraphQL To Chat With Our API!
“What’s one way you’ve used AI to simplify a task?”
There are many, but one of the most powerful examples for me was using AI to add introspection tools to our MCP Server for our GraphQL API — turning what would’ve been a complex, time-consuming process into something dynamic, scalable, and maintainable.
The Challenge: Scaling Our API Tools Without the Overhead
For some background: MCP (Model Context Protocol) acts as a bridge between AI hosts and APIs, allowing us to define “tools” that AI can call to answer user prompts.
The problem?
Our GraphQL API has hundreds of endpoints — and it’s constantly evolving. Manually defining these tools would be a maintenance nightmare. Even with AI-assisted code generation, it would still be tedious and fragile over time.
We needed a better way — something dynamic, self-updating, and AI-friendly.
The Solution: Let the API Describe Itself
That’s when we turned to GraphQL Introspection — the feature that allows an API to describe its own schema (queries, mutations, and types).
With the help of Claude, we created an MCP tool within our server that leverages GraphQL’s introspection capabilities. Instead of hardcoding endpoints, the AI can now query the API itself to discover what’s available and how to use it.
This means the AI doesn’t just call static tools — it dynamically learns what’s possible.
Building It with AI
GraphQL’s introspection docs are powerful, but not exactly verbose. Crafting and formatting every property manually would have taken a long time.
Using Claude, we generated the introspection and query execution tools almost instantly.
It automatically mapped out our schema, queries, mutations, and types — saving hours (if not days) of manual setup.
The Results: A Dynamic, Self-Aware AI System
Once this was live, we were able to test it immediately.
We asked Claude questions like:
“How many contracts have been approved in the last week, and by whom?”
Claude used introspection to discover the relevant queries, constructed the appropriate GraphQL call, and invoked it against the API — all dynamically. Within minutes, we had real results.
Before this, such a task would’ve required a developer or product team member to dig into the data manually — interrupting their work and slowing down the process.
The Impact
With AI-driven introspection inside our MCP Server, we’ve been able to:
- Save thousands of hours of data investigation and maintenance
- Empower AI to query our APIs autonomously
- Reduce developer context switching
- Lay the groundwork for future internal AI assistants that can answer business questions directly from our API
This is just the start — soon, any user in our organization, regardless of technical skill, will be able to ask natural-language questions and get answers powered by our live data through AI.
Gratitude
Huge thanks to Sergio D. Sanchez for bringing this idea to our AI roundtable and helping us set up this POC. It’s been an amazing step forward in making our systems more intelligent, adaptable, and efficient.
By blending AI, GraphQL introspection, and MCP, we’ve built a foundation where APIs can explain themselves — and AI can use that knowledge to deliver insights faster than ever before.
