Epoch AI Models
This server has a remotely hosted endpoint. Open its connector listing for connection details, authentication requirements, and setup instructions:
@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 1864+ 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 returnsfound:falsewith 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 atlimit(default 50, max 200) withcount_matching/truncatedso 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: LLM Benchmark 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_modelstrips 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),DomainandOrganization categorizationare 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'smin_computefilter andai_compute_trendboth 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 1864+ 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":"..."}.
Run it from npm
@pipeworx/mcp-epoch-ai-models is a local stdio bridge to the Pipeworx gateway: your MCP client
starts it with npx, and it forwards every call to the same
https://gateway.pipeworx.io/epoch-ai-models/mcp endpoint shown above. It needs network access;
there is no offline mode.
{
"mcpServers": {
"epoch-ai-models": {
"command": "npx",
"args": ["-y", "@pipeworx/mcp-epoch-ai-models"]
}
}
}Without a key it calls anonymously, capped at 50 calls a day. A free account
raises that to 200 a day: get a key at https://pipeworx.io/signup and add
"env": { "PIPEWORX_API_KEY": "<your key>" } to the server entry.
The key is sent as a Bearer token, the same way a hosted connection sends it,
so calls are metered and billed exactly as they are there.
It lists only this pack's tools by default. Set PIPEWORX_META_TOOLS=1 to
also list ask_pipeworx and the other gateway-wide tools.
Behind an HTTPS proxy: Node's built-in fetch does not read HTTPS_PROXY by
default. Newer Node releases honour it when NODE_USE_ENV_PROXY=1 is set; check
your Node version's documentation.
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
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Source-backed AI model pricing, rankings, history, and benchmark data.
Live registry of AI model specs, context windows, and per-token pricing.
Read-only data on new AI models and tool releases. Each fact has a source URL and a date.
Search curated open-weight AI model releases, licenses, architectures, and primary sources.
Related MCP Servers
- AlicenseAqualityFmaintenanceProvides AI assistants with real-time access to 1000+ AI models including their latest pricing, context windows, capabilities, and specifications. Supports model search, comparison, recommendations, and live testing.3166 npm1MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI agents to compare LLM benchmarks, get pricing, and receive model recommendations for tasks and budgets.25 PyPI1MIT
- AlicenseAqualityDmaintenanceEnables AI agents to query LLM and multimodal model benchmarks, pricing, speed, and track model updates via structured diffs using the Artificial Analysis public API.727 PyPI3MIT

Modelmeterofficial
AlicenseNot gradedqualityDmaintenanceEnables AI agents to query model pricing, deprecations, and cost estimates via a machine-readable changelog of the AI stack.1MIT