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Conduit publishes two plain-text artifacts that describe the entire API in a form an LLM agent can read directly: a compact index at GET /llms.txt and the full reference at GET /llms-full.txt. Both are generated from the same OpenAPI 3.1 document the API serves, so they never drift from the live surface. Neither needs an API key. An agent can fetch one cold, learn the endpoints and conventions, then start making authenticated /v1 calls.
GET /llms.txt, GET /llms-full.txt, and GET /openapi.json are all keyless, alongside GET /health and GET /ready.

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The two files

/llms.txt

A compact, sectioned index. A short preamble (base URL, auth, envelope, error codes, pagination, time model) followed by one line per endpoint grouped into sections such as “Public data (redistribution-safe)”, “Observations & provenance”, and “Calendar & official events”. Start here to learn the surface quickly.

/llms-full.txt

The full endpoint reference. Same preamble, then every endpoint with its summary, description, and parameters (types, enums, formats, and whether each is a path or query parameter). Use this when an agent needs to construct a call precisely.

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What the preamble tells an agent

Both files open with the same machine-readable conventions, so an agent gets the contract before it sees a single endpoint:
  • Base URL and version (v1).
  • Auth. Send x-api-key: <key> or Authorization: Bearer <key>. Scopes are data:read, ops:read, admin. The keyless endpoints are listed explicitly.
  • Envelope. { data, meta: { request_id, api_version, pagination }, requestId }, with the error shape and the stable codes (bad_request, not_found, unauthorized, forbidden, rate_limited, internal_error). It states plainly that unknown query params are rejected with bad_request.
  • Pagination. Cursor-based: limit (1 to 500, default 100) and cursor, with meta.pagination.next_cursor and has_more.
  • Time model. How period / period_start / period_end, start_date / end_date, and as_of differ.
  • Read-only. Every endpoint is GET.

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Fetch them

# Compact index (no key required)
curl "https://data.quantoraresearch.com/llms.txt"

# Full reference with parameters (no key required)
curl "https://data.quantoraresearch.com/llms-full.txt"

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How an agent uses them

Discover

Fetch /llms.txt to read the conventions and scan the endpoint index. This is enough to choose the right endpoint for a question (for example, /v1/public/observations/latest for the latest reading, or /v1/public/screener to filter countries).

Construct

Fetch /llms-full.txt (or /openapi.json for the structured spec) to read the exact parameters, types, and enums for the chosen endpoint, then build a valid request. Because unknown params are rejected, getting names right up front avoids a bad_request.

Call and follow

Make the authenticated /v1 request with your key. The response is the standard envelope. For list endpoints, follow meta.pagination.next_cursor while has_more is true.
For programmatic consumers (Postman, Insomnia, code generators, or your own tooling), the structured OpenAPI 3.1 document at GET /openapi.json is the canonical source the text files are projected from. Use the text files for agents that read prose, and the OpenAPI document for machine import.

MCP server

Skip the manual HTTP and give your agent native tools instead.

TypeScript SDK

A typed client over the same envelope and errors.