Skip to main content

AI Integration

Purse documentation is available to AI coding assistants through two complementary mechanisms. Both are designed to help your agent understand the API and generate accurate integration code — they differ in how the agent accesses the content.

Which approach to use?​

MCP ServerLLMs.txt
How it worksLive semantic search + OpenAPI access via 6 dedicated toolsStatic curated overview, loaded as conversation context
Response qualityPrecise, page-level answers with code examplesBroad structural overview of the platform
Tool supportMCP-compatible agents: Claude Code, Claude Desktop, Cursor, Windsurf, Gemini CLIAny AI tool that can read a URL
Best forWriting and debugging integration codeQuick orientation without tools
Setup time~2 min~1 min
Recommended: start with MCP

If your AI agent supports MCP, use the MCP server. It gives your agent real-time access to the exact content it needs — including full OpenAPI spec inspection — and is purpose-built for LLM workflows.

When to use MCP​

Use the MCP server when:

  • Your AI agent supports the MCP protocol (Claude Code, Cursor, Windsurf, Gemini CLI, Claude Desktop, etc.)
  • You're actively building a Purse integration and need precise, page-level answers
  • You want to query specific API endpoints, request/response schemas, or edge cases
  • You need live documentation that always reflects the latest version

When to use LLMs.txt​

Use LLMs.txt when:

  • Your AI agent doesn't support MCP (ChatGPT, Gemini, basic chat interfaces)
  • You want a lightweight doc overview to paste into any conversation
  • You need a quick platform orientation before diving into specifics

Use both together​

For the best results, combine the two:

  • Load LLMs.txt once to give your agent a structural overview of the platform
  • Connect the MCP server for precise lookups while coding

Both are free and require no authentication.