@pipeworx/epoch-ai-models

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.

Part of Pipeworx — an MCP gateway connecting AI agents to 1800+ live data sources. This is an independent, unofficial integration — not affiliated with, endorsed by, or published by the upstream provider.

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.

Related MCP server: AgentLayer MCP Server

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.

Quick Start

Add to your MCP client (Claude Desktop, Cursor, Windsurf, etc.):

{
  "mcpServers": {
    "epoch-ai-models": {
      "url": "https://gateway.pipeworx.io/epoch-ai-models/mcp"
    }
  }
}

What this endpoint actually serves

tools/list at https://gateway.pipeworx.io/epoch-ai-models/mcp returns the tools in the table above plus the shared Pipeworx meta-tools — ask_pipeworx, discover_tools, search_within, remember/recall and the rest of the gateway-wide set. So the tool count you see is larger than this table: a single-pack endpoint currently lists roughly 30 shared tools alongside the pack's own. The connection's initialize response states its exact scope, and is the authoritative answer for a given day.

This is deliberate, not multiplexing by accident. The meta-tools are what let a scoped connection answer a question this pack does not cover — via ask_pipeworx, which routes across the whole catalog — without you adding a second MCP server. There is currently no way to mount a pack endpoint without them; if the extra schemas cost you more context than the routing is worth, connect to the full gateway once rather than to several pack endpoints.

Or connect to the full Pipeworx gateway to get every pack's tools listed directly, instead of just this one's:

{
  "mcpServers": {
    "pipeworx": {
      "url": "https://gateway.pipeworx.io/mcp"
    }
  }
}

Both URLs reach the same gateway and the same 1800+ data sources. The only difference is which pack's tools are listed directly; ask_pipeworx reaches all of them from either one.

No MCP client? Call it over HTTP

curl -X POST https://gateway.pipeworx.io/v1/tools/ai_model \
  -H 'Content-Type: application/json' \
  -d '{"name":"GPT-4"}'

No account needed for the first calls. Inspect any tool: GET https://gateway.pipeworx.io/v1/tools/ai_model. Find one: POST https://gateway.pipeworx.io/v1/tools/search_packs with {"query":"..."}.

Standalone (no gateway account)

This package also runs as a local stdio MCP server — no Pipeworx account, no gateway round-trip:

{
  "mcpServers": {
    "epoch-ai-models": {
      "command": "npx",
      "args": ["-y", "@pipeworx/mcp-epoch-ai-models"]
    }
  }
}

Or run it directly to confirm it starts:

npx -y @pipeworx/mcp-epoch-ai-models

It speaks MCP over stdin/stdout and answers initialize/tools/list/tools/call for only this pack's tools — none of the shared meta-tools the gateway connection above adds. Same source, same tools, no ask_pipeworx routing.

Using with ask_pipeworx

Instead of calling tools directly, you can ask questions in plain English — this works on the pack endpoint above as well as on the full gateway:

ask_pipeworx({ question: "your question about Epoch Ai Models data" })

The gateway picks the right tool and fills the arguments automatically.

More

• Docs and guides

• pipeworx.io

License

MIT