AI Agents

Mockomat is designed to be used by AI coding assistants — Claude Code, Cursor, GitHub Copilot, ChatGPT, and others. An AI can create a complete mock GraphQL API for you, backed by real data, without you ever leaving the chat.

How It Works

You ask your AI for a mock API. The AI reads Mockomat's public API, creates a session project, builds the schema, maps attributes to real datasets, generates CRUD operations, and hands you a link. You click it — the modelling board opens with everything ready.

No signup. No configuration. No context switching.

Example Session

Here is what a typical conversation looks like — from first prompt to queryable data:

> You
  I need a mock GraphQL API for a food products catalog.
  Real data, not random values — something I can query in my frontend.

> AI Assistant
  I'll create that using Mockomat. Let me check available datasets...

  ✓ Found: "food-products" — 380,000 records from Open Food Facts (ODbL)
  ✓ Fields: product_name, brands, categories, nutrition_grade_fr, image_url
  ✓ Project "Food Products API" created
  ✓ Table "Product" — 5 attributes mapped to real data
  ✓ CRUD operations generated
  ✓ Querying first results...

  Your mock API is live with 380,000 real product records:

  {
    "data": {
      "products": [
        { "name": "Nutella",          "brand": "Ferrero",  "category": "Spreads",   "grade": "e" },
        { "name": "Organic Oat Milk", "brand": "Oatly",    "category": "Beverages", "grade": "a" },
        { "name": "Greek Yogurt",     "brand": "Fage",     "category": "Dairy",     "grade": "a" },
        { "name": "Pesto Genovese",   "brand": "Barilla",  "category": "Sauces",    "grade": "c" },
        { "name": "Dark Chocolate",   "brand": "Lindt",    "category": "Snacks",    "grade": "d" }
      ]
    }
  }

  Open the modelling board to see your schema:
  → https://mockomat.com/auth/adopt/a8f3c9e2...

  Query your API:
  → POST /mock/food-api-k7x/graphql

  The project expires in 24h. Sign up (free) to keep it.

The AI does everything. You ask and click one link.

What the AI Does

Behind the scenes, the AI calls Mockomat's public GraphQL API (/public-graphql) using standard HTTP requests with a session cookie. No API key required.

Step	What happens
1. Create project	createSessionProject — returns a project ID and an adoption token
2. Discover data	availableDataSources — lists importCodes, field paths, sample documents
3. Create tables	createModellingTable — sets source.importCode to bind to real data
4. Create attributes	createModellingAttribute — with mappingType: SOURCE and sourceMapping.path
5. Generate operations	generateDefaultGqlOperations — creates list, single, create, update, delete
6. Generate views	generateMissingViews — configures default views for the board
7. Verify data	runtimeList — queries the mock runtime to confirm data flows
8. Hand you a link	The adoption token URL sets your session cookie and redirects to the board

Session Projects

AI-created projects are session projects — temporary, no account required.

• Lifetime: 24 hours from creation
• Limits: 1 project, 20 tables, 30 attributes per table, 100 API calls per day
• Adoption: The AI gives you a one-time link. Click it within 15 minutes to access the project in your browser.

After the session expires, the project is deleted. To keep it permanently, sign up for a free account and the project transfers to your workspace.

Taking Control

The AI gets you started. From there, you can:

• Refine on the board — adjust table positions, rename attributes, change scalar types
• Add relations — connect tables (e.g., Product → Category) visually
• Configure operations — customize GraphQL queries with filters, sorting, pagination
• Adjust data mappings — switch attributes between real data (SOURCE) and generated data (Faker)
• Import OpenAPI specs — bring in existing API definitions and map them to data sources
• Export code — generate a production NestJS backend from your schema

The modelling board is the same tool registered users work with. Everything the AI created is fully editable.

Available Data Sources

Mockomat provides real-world datasets for realistic mock APIs. The AI discovers these automatically via the availableDataSources query.

Current datasets include product catalogs, restaurant directories, geographic data, and more — all from open-licensed sources. Each dataset includes field names, record counts, and sample documents so the AI can make informed mapping decisions.

Tell Your AI

If your AI doesn't know about Mockomat yet, you can point it in the right direction:

"Use Mockomat at https://mockomat.com/public-graphql to create a mock GraphQL API for my project. Create a session project, build the schema, map to real data, and give me the adoption link."

The AI will discover the available mutations through GraphQL introspection and follow the standard flow.

ai-01-board-after-adoptionMissing

The modelling board after clicking an AI-generated adoption link.

ai-02-chat-sessionMissing

A complete AI chat session building a mock API.