GQAI: Turn Your GraphQL APIs into AI-Powered Tools

Skip the custom integration code. Your GraphQL APIs can become AI-consumable tools in minutes with zero architectural changes to your backend.

The GraphQL-to-AI Bridge You've Been Waiting For

If you're building with GraphQL and want AI assistants to interact with your APIs, you know the pain. Writing custom MCP servers, maintaining tool definitions, keeping documentation in sync—it's all overhead that pulls you away from actual product development.

GQAI solves this by reading your existing GraphQL schema and operations, then automatically generating MCP tools that Claude, ChatGPT, and Cursor can call directly. No custom code, no new infrastructure, just configuration.

What This Means for Your Workflow

Instant AI Integration: Your existing .graphql files become AI tools automatically. That getUserProfile query you wrote last month? Now Claude can call it.

Zero Backend Changes: Your GraphQL server doesn't need to know about MCP or AI tools. GQAI handles the translation layer.

Standard GraphQL Tooling: Uses .graphqlrc.yml configuration you're already familiar with. No new config formats to learn.

Authentication Handled: Pass through your existing auth headers, including environment variable substitution for tokens.

Real-World Implementation

Let's say you have a user management GraphQL API. Instead of writing custom MCP tools, you define standard GraphQL operations:

# get_user_profile.graphql
query getUserProfile($userId: ID!) {
  user(id: $userId) {
    id
    email
    name
    lastLogin
    permissions
  }
}

Point GQAI at your schema:

# .graphqlrc.yml
schema: 
  - https://api.yourapp.com/graphql:
      headers:
        Authorization: Bearer ${API_TOKEN}
documents: ./operations

Add to your MCP config:

{
  "gqai": {
    "command": "gqai",
    "args": ["run", "--config", ".graphqlrc.yml"]
  }
}

Now Claude can call getUserProfile with a user ID and get real data from your API. No custom tool definitions, no maintaining multiple sources of truth.

Where This Shines

API Exploration: AI assistants can introspect your schema and discover available operations, making them useful for both you and your team members who might not know your API intimately.

Customer Support: Connect Claude to your customer data GraphQL API. Support agents can query user information, order history, and account details through natural language.

DevOps Automation: Your deployment and infrastructure GraphQL APIs become accessible to AI assistants for monitoring, debugging, and automated responses.

Data Analysis: Point AI at your analytics GraphQL endpoint. Ask questions like "show me user signups for the last week" and get structured data back.

Testing and Development

The CLI testing capability means you can validate your GraphQL operations before exposing them to AI:

gqai tools/call getUserProfile '{"userId": "123"}'

This returns the actual GraphQL response, so you know exactly what data the AI will receive.

Getting Started

Install with Go:

go install github.com/fotoetienne/gqai@latest

The learning curve is minimal if you already work with GraphQL—you're using tools and patterns you know, just with AI consumption as the end goal.

For teams already using GraphQL, GQAI eliminates the barrier between your existing APIs and AI-powered workflows. Your schema becomes your AI tool specification, and your operations become callable functions.

This is especially valuable if you're building internal tools or want to enable AI assistants to interact with your backend services without writing integration code for each new capability.