Docs for LLMs

This documentation is published in formats that AI assistants can read directly, so you can hand an LLM the full Pipecorn API reference instead of pasting pages one at a time.

The files

File	What it contains	Use it when
`llms-full.txt`	Every page of this documentation concatenated into one file, including endpoint references, error codes, and the credits vs limits guide	You want the model to know the whole API in one shot
`llms.txt`	A short index: one line per page with a link and a summary	You want a small context footprint and let the model fetch what it needs
`agents.md`	Agent-facing guide: which surface to use (MCP, REST, CLI), conventions to follow, auth	You are building an agent that acts on Pipecorn, not just reads about it

Both llms.txt files follow the llms.txt convention and are regenerated on every docs deploy, so they always match the pages you see here.

https://docs.pipecorn.com/llms-full.txt
https://docs.pipecorn.com/llms.txt
https://docs.pipecorn.com/agents.md

How to use them

Claude or ChatGPT
Claude Code
Cursor
Your own agent

Paste the llms-full.txt URL into the conversation and ask your question. Both assistants fetch the file and answer from it. For long sessions, attach it to a Project so it stays in context.

Ask Claude Code to read the file, or point it at the URL in a prompt:

Read https://docs.pipecorn.com/llms-full.txt, then write a script that
enriches the contacts in leads.csv with the Pipecorn bulk enrichment endpoint.

Add the URL to your project’s CLAUDE.md if you integrate Pipecorn regularly.

Open Settings → Features → Docs, click Add new doc, and paste https://docs.pipecorn.com/llms-full.txt. It then shows up under @Docs in chat.

Fetch llms-full.txt at build time or on first use and put it in the system prompt or a retrieval index. Fetch llms.txt instead when context is tight, and let the agent follow links.

Reading vs acting

These files let a model read about the API. If you want the model to call Pipecorn (search companies, extract leads, enrich contacts) from a chat, connect the MCP server instead. The two work well together: the MCP server exposes the tools, and llms-full.txt explains the concepts behind them, such as the difference between credits and limits. Machine-readable discovery for the MCP server lives at /.well-known/mcp.json.

First steps

Build with AI

Resources

The files

How to use them

Reading vs acting

​The files

​How to use them

​Reading vs acting

The files

How to use them

Reading vs acting