Docs for AI agents (MCP)

A lot of Data Workers usage happens inside an AI agent — so the documentation is built to be read by one. Three mechanisms, lightest first.

llms.txt

The whole site is indexed for language models:

/llms.txt — the index: every page with its one-line description.
/llms-full.txt — the full corpus: every page’s complete content in one plain-text file.

Point any agent at either URL. For a one-off question, fetching llms-full.txt and asking against it is usually enough.

The docs MCP server

For agents that work with Data Workers regularly, the docs ship as an MCP server with three tools:

Tool	What it does
`search_docs`	Full-text search over the documentation; returns matching pages with excerpts
`read_doc`	Returns a page’s complete content by slug
`submit_feedback`	Files a bug report, feature request, or incident — same pipeline as the feedback form

Install (Claude Code):

claude mcp add dataworkers-docs -- npx -y @dataworkers/docs-mcp

Any other MCP client: command npx -y @dataworkers/docs-mcp (stdio). The server bundles the doc corpus at publish time and needs no network for search/read; submit_feedback POSTs to the docs feedback endpoint.

Then, in your agent:

Search the Data Workers docs for how to verify a Snowflake connection.

File a bug with Data Workers: the connection test crashes when SNOWFLAKE_ACCOUNT contains a region suffix.

The product MCP surface

The product itself pushes context to agents too: the Data Workers MCP servers describe their capabilities in their MCP handshake (the instructions field), so a connected coding agent knows what the fleet can do without reading this site at all. These docs are the depth behind that: onboarding, connector setup, and the honest capability edges.