Building Connectors with AI: From API Docs to Live Sync

Traditional integration platforms require weeks of development to add a new connector. You study the API docs, write the authentication flow, handle pagination, map fields, test edge cases, and deploy. Then you do it again for the next platform.

PullPush uses a fundamentally different approach: AI generates the connector definition, humans verify, and the deterministic engine runs it.

The MCP-powered workflow

PullPush exposes 80+ Model Context Protocol (MCP) tools that let AI assistants interact with the sync engine directly. Here's how a new connector gets built:

1. Discovery

The AI reads the platform's API documentation and discovers available endpoints, authentication methods, and data schemas.

2. Generation

Based on the discovered API, the AI generates a connector definition — a JSON document that describes:

• How to authenticate (Bearer token, OAuth 2.0, API key)
• Which endpoints to call for each data type
• How to map native fields to the canonical model
• Pagination strategy (cursor, offset, page)
• Rate limiting configuration

3. Validation

The definition is validated against PullPush's connector schema. This catches structural errors before any API call is made.

4. Testing

The AI runs the connector against the live API (in read-only mode) to verify:

• Authentication works
• Endpoints return expected data
• Field mapping produces valid canonical events
• Pagination reaches all records

5. Publishing

Once verified, the connector is published to the marketplace. Other users can install it with one click.

Why this works better

The key insight is that connector definitions are declarative, not imperative. The AI doesn't write code that runs in production — it writes a JSON configuration that the deterministic engine interprets.

This means:

• The connector is inspectable — you can read the JSON and understand exactly what it does
• It's version-controlled — changes are tracked in git
• It's testable — the validation schema catches errors before deployment
• It's safe — the engine enforces security boundaries regardless of what the definition says

From weeks to minutes

What used to take a development team weeks now happens in a single AI session:

1. Point the AI at the API documentation
2. It generates and tests the connector
3. Review the definition (it's just JSON)
4. Publish to the marketplace

Every new connector automatically works with every destination in the system — thanks to the canonical data model.