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llms.txt, Markdown and discovery files

File What Size
/llms.txt (also /docs/llms.txt) What Chord is, then every page of this documentation with a one-line description, the API, the agent integrations and the app (llmstxt.org format) small
/docs/<page>.md Any page as Markdown: add .md to the page’s path (/docs/agents/mcp/ → /docs/agents/mcp.md), or ask for the page with Accept: text/markdown. API operations too (/docs/api-reference.md lists them) per page
/llms-full.txt (also /docs/llms-full.txt) Every page of this documentation, concatenated large
/docs/llms-small.txt The same, minified — for a context window that needs the whole thing medium
/openapi.json (also /docs/openapi.json) The OpenAPI 3.1 contract: every endpoint, parameter, schema and example large

An agent that only knows getchord.xyz finds everything else from these:

File Standard Points to
/.well-known/api-catalog RFC 9727 (linkset) the REST API with its OpenAPI document, docs and status; both MCP servers; the skill index
/.well-known/ai-catalog.json AI Catalog (MCP SEP-2127) the server cards of the MCP server and the Docs MCP
/.well-known/agent-skills/index.json Agent Skills discovery the Chord skill archive and its SHA-256 digest
/robots.txt robots + Content Signals crawlers, assistants and agents allowed (search, ai-input, ai-train = yes); both sitemaps

api.getchord.xyz answers /.well-known/api-catalog, /.well-known/ai-catalog.json and /robots.txt too, and GET https://api.getchord.xyz/ returns the same links as JSON.

Paste llms.txt and let the model fetch the .md pages it needs, or paste llms-small.txt when the model must answer without fetching. Add the two facts models get wrong most:

Chord API base: https://api.getchord.xyz/v1. Amounts are decimal strings in the token's smallest unit.
On Arc (chain 5042) USDC is the gas token; send the ERC-20 predeploy 0x3600000000000000000000000000000000000000 (6 decimals); the zero address is an alias for it and transactions always have value 0.

Give it openapi.json. Every operation has an operationId, constraints on parameters (enums, ranges, patterns), a response schema, and a real example captured from the live API.

Fetch llms-full.txt and split on the page separators; each page starts with its title and URL. Refresh daily — the file is rebuilt with every deploy of the docs.