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Every model on varg has a machine-readable spec, generated from the live catalog: its inputs and their limits, provider options, routes, prices, and the exact request to send over REST or MCP. Point your agent at it and it calls any model without guessing field names.

All models

varg.ai/models/llms.txt — every model, one line each, grouped by capability.

One model

varg.ai/models/{model}.md — the full spec of one model.

Everything

varg.ai/models/llms-full.txt — every spec in one file, for long-context agents.

JSON Schema

GET /v2/tools/{tool}?model={model} — the exact input schema. No API key needed.
The specs follow the llms.txt convention. They are regenerated within an hour of a catalog change, so they can be trusted over model IDs or prices remembered from training data.

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Call a model

The same request body works on the REST API and in MCP.
The call answers 202 with a job. Poll GET /v2/jobs/{id} until status is completed, then read output.outputs[0].url — or pass options.webhook_url.

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From the Playground

Every model’s page in the Playground has the same surfaces:
  • API — the request the form would send right now, as cURL, JavaScript or Python.
  • MCP — the exact call_tool for those parameters, and the client setup.
  • Ask — hands the request to Claude Code, Claude, ChatGPT, Codex, Devin or Cursor with a ready prompt that links the model’s spec.
  • Schema and LLMs — the JSON Schema and the Markdown spec.

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Discover at runtime

Agents can walk the catalog without a key:
Price a request before running it with POST /v2/estimate.
Pipelines are private to the teams they belong to and never appear in the public specs. A signed-in caller with access sees them in GET /v2/tools/pipeline/models.