Epoch Ai Models
NPM · @PIPEWORX/MCP-EPOCH-AI-MODELS · 2 COMPONENTS
Epoch AI — AI models database
Available components
Last scanned (12 hours ago)
Scores higher than 76% of npm servers
Category AI and Machine Learning
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security100
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- No production dependencies, so there is no dependency health to assess. View diagnostics → Pass
Provenance & Transparency97
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to pipeworx-io/mcp-epoch-ai-models). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 3 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability69
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 504 tokens (~168/item across 3 items; 3 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management23
- Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 3 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 4 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 4 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 17 to 20. That category is still filling its 30-day observation window: 5 days of observed history at the previous scan, 6 at this one. The score rises as the window fills, whether or not the server changes.
- 2 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 10 to 13. That category is still filling its 30-day observation window: 3 days of observed history at the previous scan, 4 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 +1
- Tool safety: pass → unverified ▼ security
- Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet. security
- Tool coverage: 100 → unverified ▼ functional
- Capabilities: pass → unverified ▼ functional
- Stability: unverified → 0.10 ▲ functional
- First check of Schema quality: unverified functional
- Package version: 0.2.0 → 0.2.1 functional
- 30 Oct 26 0
- Security disclosure: unverified → fail ▼ functional
- 29 Oct 26 80
First indexed and scored.
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 5 Nov 2026 · Analysed npm/@pipeworx/mcp-epoch-ai-models@0.2.1
Provenance Verified
A signed build attestation was found and verified, binding this exact artifact to the source repository it claims to come from.
| Result | Verified |
|---|---|
| Ecosystem | npm |
| Reason | Verified |
| Discovered via | Registry attestation endpoint |
| Source repo | pipeworx-io/mcp-epoch-ai-models |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/pipeworx-io/mcp-epoch-ai-models/.github/workflows/publish.yml@refs/heads/main |
| Rekor log index | 3635926013 |
| Predicate type | SLSA build provenance https://slsa.dev/provenance/v1 |
| Subject digest | sha512:a9efac440b5d27d4fe07ada147275f2e76d9a2ce69e91216488c01c2b8f708f4290efeb019ccf4ec3c0c32eff051893f71d7680285b4cbf247f0be9fb |
Background: How many MCP packages publish verified provenance →
Dependencies 0 packages
| Packages resolved | 0 |
|---|---|
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
ai_compute_trend ~103
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. Optionally filtered by domain and a starting year.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | – | Case-insensitive substring filter on domain, e.g. "Language" |
| from_year | number | – | Earliest calendar year to include, default 2010 |
No output schema declared.
No examples provided.
ai_model ~129
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-4" matches Epoch AI's "GPT-4 (Mar 2023)" row). An unmatched name returns found:false with the closest names in the dataset, never an empty result.
| Name | Type | Req | Description |
|---|---|---|---|
| name | string | yes | Model name, e.g. "GPT-4", "Gemini 1.5 Pro", "Llama 3.1 405B" |
No output schema declared.
No examples provided.
ai_models_search ~215
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, capped at `limit` (default 50) with count/truncated so a large match set is never silently cut.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | – | Case-insensitive substring filter on domain, e.g. "Language", "Vision", "Speech" |
| limit | number | – | Max rows to return, default 50, max 200 |
| min_compute | number | – | Minimum training compute in FLOP, e.g. 1e25 |
| offset | number | – | Row offset for paging, default 0 |
| organization | string | – | Case-insensitive substring filter on organization name, e.g. "OpenAI" |
| query | string | – | Case-insensitive substring filter on model name |
| year | number | – | Restrict to models published in this calendar year |
No output schema declared.
No examples provided.
How do I install the Epoch Ai Models MCP server?
Epoch Ai Models runs locally as an npm package, launched with npx -y @pipeworx/mcp-epoch-ai-models. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · @pipeworx/mcp-epoch-ai-models
claude mcp add pipeworx-io-epoch-ai-models -- npx -y @pipeworx/mcp-epoch-ai-models
What is the Epoch Ai Models MCP server?
Epoch Ai Models is an MCP server listed in the public MCP registry as io.github.pipeworx-io/epoch-ai-models. Epoch AI, AI models database. This page covers its npm package (@pipeworx/mcp-epoch-ai-models).
Is the Epoch Ai Models MCP server safe to use?
Epoch Ai Models scores 83 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 November 2026. It declares no install or post-install scripts. Its build provenance is signed and verified. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the Epoch Ai Models MCP server expose?
Epoch Ai Models exposes 3 tools: ai_model, ai_models_search, ai_compute_trend. Their descriptions and schemas cost roughly 447 tokens of context every time the server is loaded.
Is the Epoch Ai Models MCP server still maintained?
Epoch Ai Models is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the Epoch Ai Models MCP server under?
Epoch Ai Models declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.