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Your AI coding agent can connect directly to this documentation and look up endpoints, request parameters, and integration guidance on demand, rather than from what it already knows. Pick whichever fits your workflow: a live connection over MCP, a plain-text copy of the docs, or an installable Agent Skill. Every page also has a contextual menu, top right, with one-click actions to copy the page as Markdown, open it in Claude or Google AI Studio, open it in VS Code, or connect over MCP. None of them need manual setup. The sections below cover the manual path for each.

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Connect over MCP

Connecting over MCP gives your agent a live, searchable connection to this documentation. To connect manually, add the Yapily docs MCP server to your agent’s configuration. The file and field names vary slightly by tool:
Once connected, your agent can search this documentation, fetch specific pages, and cite what it finds. No authentication is required.

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Reference the full docs as text

If your workflow doesn’t support MCP, you can point an agent directly at a plain-text version of this site:
  • /llms.txt: an index of every page with a short description, useful for agents that want to find the right page before reading it
  • /llms-full.txt: the complete content of this documentation in a single file, useful when you want to give an agent full context in one request

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Agent Skills

This site also publishes a /skill.md file describing what Yapily’s API does, its core authorisation workflow, and key constraints such as consent token lifetimes and payment reference limits. Agents and tools that support the Agent Skills format can discover it automatically, or install it directly:

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Example prompts

Once your agent is connected, try prompts like:
  • “Using the Yapily docs, show me how to create a single payment authorisation request.”
  • “Look up the Yapily error codes for a 500 response from the payments API.”
  • “What’s the consent token expiry for UK accounts, according to the Yapily docs?”
Agents should treat the OpenAPI specification as the source of truth for exact request and response shapes, and this documentation for integration guidance and context.