@pipeworx/epoch-ai-models

Connect: https://pipeworx.io/mcp — every tool in the catalog, including @pipeworx/epoch-ai-models’s. Install: one-click buttons

No MCP client? Skip the connection: POST https://gateway.pipeworx.io/v1/tools/search_packs {"query":"..."} to find a tool below, GET /v1/tools/<name> for its schema, POST the same URL with arguments for the data — see For AI agents.

Tools: 3

AI model facts — organization, release date, parameter count, training-compute (FLOP) estimate, hardware, accessibility — looked up by name, searched by organization/domain/year/compute threshold, or aggregated into a yearly training-compute trend.

Tools

• ai_model(name) — one model’s organization, release date, parameter count, training-compute estimate, hardware and accessibility. Resolves dated variants (a query for “GPT-4” matches the dataset’s “GPT-4 (Mar 2023)” row). An unmatched name returns found:false with the closest names in the dataset, never an empty result.
• ai_models_search(organization?, domain?, year?, min_compute?, query?, limit?, offset?) — filter the dataset by organization, domain (e.g. “Language”, “Vision”), publication year, and/or a minimum training-compute (FLOP) threshold. Newest-first, capped at limit (default 50, max 200) with count_matching/truncated so a large match set is never silently cut.
• ai_compute_trend(domain?, from_year?) — per-year count of models with a compute estimate and the largest training run (FLOP) that year, optionally filtered by domain and a starting year.

Auth

Keyless.

Data sources

• Epoch AI, “Data on AI models” — Epoch AI’s dataset of notable machine learning models and their organization, release date, parameters, training compute, hardware and accessibility.

Licence: Epoch AI publishes this data under CC BY — “free to use, distribute, and reproduce provided the source and authors are credited under the Creative Commons Attribution license.” Every tool response carries that attribution plus data_as_of (when this isolate last pulled the dataset from Epoch AI).

Notes

• Epoch AI names dated variants with a trailing parenthetical (e.g. “GPT-4 (Mar 2023)”, “GPT-4 Turbo (Nov 2023)”). ai_model strips that qualifier to resolve a bare “GPT-4”-style query to the canonical release; when more than one dated variant shares a base name, it picks the earliest one that carries a training-compute estimate.
• Organization, Country (of organization), Domain and Organization categorization are comma-separated multi-value columns upstream (a model can list more than one organization or domain); every tool returns these as arrays and filters by substring/membership rather than exact string equality.
• Not every row carries a training-compute or parameter estimate — many entries are notable for other reasons (benchmark results, accessibility) and leave those fields blank upstream. ai_models_search’s min_compute filter and ai_compute_trend both treat a blank estimate as “excluded,” not zero.

Tools

• ai_model — Look up one AI model in Epoch AI’s models database by name — organization, release date, parameter count, training-compute (FLOP) estimate, hardware, accessibility. Resolves dated variants (e.g. “GPT-
• ai_models_search — Search/filter Epoch AI’s models database by organization, domain (e.g. “Language”, “Vision”, “Multimodal”), publication year, and/or a minimum training-compute (FLOP) threshold. Returns newest-first,
• ai_compute_trend — Yearly training-compute trend from Epoch AI’s models database: for each year, the number of models with a compute estimate, the largest training run (FLOP) that year, and which model set it. Optionall