Use with AI tools

If you’re building with Pathbound from inside an AI assistant — Claude, ChatGPT, or a homegrown agent — you can point it at machine-readable copies of these docs so it answers using current product knowledge instead of guessing.

We publish three formats following the llms.txt convention:

URL	What it is	When to use it
`https://pathbound.ai/llms.txt`	Curated index — page titles, descriptions, and links to each doc.	Agents that pull docs on demand (custom RAG, retrieval tools).
`https://pathbound.ai/llms-full.txt`	The full docs concatenated as one plain-text bundle.	One-shot ingestion (Claude Projects, Custom GPT knowledge).
`https://pathbound.ai/docs/<page>.md`	The raw markdown for any single page.	Targeted fetches when an agent already knows what it needs.

All three are generated from the same source as the human-facing docs at /docs/* and update on every deploy.

Claude.ai / Claude Projects

Open a Claude Project.
Add https://pathbound.ai/llms-full.txt as a knowledge source (paste the URL into Add content → URL).
Ask questions normally — Claude grounds its answers in the docs.

For interactive access to your live data (contacts, companies, events) — not just docs — connect the Pathbound MCP server instead. The two complement each other: llms.txt gives Claude reference knowledge, MCP gives it your tenant’s data.

ChatGPT / Custom GPTs

Download https://pathbound.ai/llms-full.txt.
Upload it as a knowledge file in the Custom GPT builder.
Add an instruction like: “When answering Pathbound questions, cite the relevant section from the uploaded docs.”

Your own agent

Any URL under /docs has a sibling .md that returns clean markdown:

curl https://pathbound.ai/docs/api/contacts.md
curl https://pathbound.ai/docs/mcp/tools.md
curl https://pathbound.ai/docs/tracker/install.md

Or fetch the full bundle in one request:

curl https://pathbound.ai/llms-full.txt

Both return text/plain (or text/markdown for per-page mirrors) — no HTML to strip, no JavaScript to render.

Two paths, one surface

Pathbound exposes itself to agents in two ways:

Reference path (these docs). Static product knowledge — endpoints, fields, examples. Fed via llms.txt. Doesn’t change per request.
Live path (MCP server). Interactive access to your tenant’s contacts, companies, and events. Authenticated, real-time.

Most useful agents end up using both: the docs to know how the API works, the MCP server to actually call it.