io.github.bikemeardsley/where-my-hug
NPM · WHERE-MY-HUG
Which parts of an ML need Hugging Face already covers. Every model and dataset checked live.
Available components
Last scanned (14 hours ago)
Scores higher than 92% 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 Security98
- 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
- 30 of 93 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency100
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Cryptographically verified build provenance (signed, bound to bikemeardsley/where-my-hug). View diagnostics → Pass
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 8 days ago).Pass
- Publishes a security disclosure policy (SECURITY.md).Pass
Schema Quality & AI Usability60
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 665 tokens (~221/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 Management73
- Stability observed for 22 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage93
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 80% of tool parameters carry a description.Partial
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 3 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
Checks we ran (16)
- Malware scan
- Known CVEs
- Install scripts
- Dependency health
- Source repository
- Provenance
- License
- Maintenance
- Security disclosure
- Schema quality
- Stability
- Tool coverage
- Injection markers
- Destructive annotations
- Judged manipulation
- MCP protocol
Does not apply to this package (2)
- Endpoint SecurityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
- Transport & ReachabilityScored on hosted endpoints only. A package has no live endpoint of its own for us to probe.
The score does not measure everything. What the score does not measure →
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.
- 5 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 70 to 73. That category is still filling its 30-day observation window: 21 days of observed history at the previous scan, 22 at this one. The score rises as the window fills, whether or not the server changes.
- 3 Nov 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 63 to 67. That category is still filling its 30-day observation window: 19 days of observed history at the previous scan, 20 at this one. The score rises as the window fills, whether or not the server changes.
- 1 Nov 26 0
- Package version: 0.2.0 → 0.3.1 functional
- 31 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.
- 29 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.
- 28 Oct 26 +26
- CVE-2023-54404 no longer affects this package ▲ security
- Malware scan: unverified → pass ▲ security
- Known CVEs: unverified → pass ▲ security
- Dependency health: unverified → 0.85 ▲ functional
- 27 Oct 26 −20
- Known CVEs: fail → unverified ▼ security
- Malware scan: pass → unverified ▼ security
- Schema quality: 192 → 221 ▼ functional
- Dependency health: 0.85 → unverified ▼ functional
- Package version: 0.2.1 → 0.3.1 functional
- 25 Oct 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.
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/where-my-hug@0.3.1
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 | bikemeardsley/where-my-hug |
| Certificate issuer | https://token.actions.githubusercontent.com |
| Certificate SAN | https://github.com/bikemeardsley/where-my-hug/.github/workflows/release.yml@refs/tags/v0.3.1 |
| Rekor log index | 3504502719 |
| Predicate type | SLSA build provenance https://slsa.dev/provenance/v1 |
| Subject digest | sha512:d28c6079734681bf17eb4d0f7f08448aa2c32a8543bb6e7a8d312d2f190d8f90d91c55b2bf3e3fdcd748c5f660f1503e44c63219e6ce593990ba8cdfa |
Background: How many MCP packages publish verified provenance →
Dependencies 93 packages
| Packages resolved | 93 |
|---|---|
| Stale | 30 |
| 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 →
where_my_hug Where my hug at? ~270
Step 1 of 3, before recommending any machine learning model or dataset. If compute, license, stack or data is missing, returns the questions to ask the user first: call again with the answers ("unknown" is fine). Then returns the method: split the ML need into parts, search Hugging Face and the other hubs for each with where_my_hug_search, and decide from what it finds which parts they cover (use a model as is, fine-tune one, train one) and which are plain code. Follow it.
| Name | Type | Req | Description |
|---|---|---|---|
| compute | string | – | The hardware it has to run or train on, such as "one 24 GB GPU" or "CPU only". Size flags take the first "N GB" in it as the memory a model must fit in |
| data | string | – | Training data there is or could be, such as "none" or "about 300 labeled photos" |
| license | string | – | What the result may be used for, such as "commercial, closed source" or "research only" |
| need | string | yes | The ML need in plain words: what goes in, what should come out, and what it is for |
| stack | string | – | What will call the model: language, runtime, framework |
No output schema declared.
No examples provided.
where_my_hug_search Where my hug at? (searched live) ~131
Step 2 of 3: searches Hugging Face for each part of the need (the task's most downloaded and trending, names holding each keyword, and the text of model and dataset cards), and Kaggle, Zenodo, ModelScope and Roboflow Universe, for models and datasets alike. One found on several hubs is listed once, naming the others. Returns what it found, with downloads, likes, license, size, age and how each turned up, to pick each part's verdict and candidates from. Then call where_my_hug_verify.
| Name | Type | Req | Description |
|---|---|---|---|
| parts | array | yes | – |
No output schema declared.
No examples provided.
where_my_hug_verify Where my hug at? (checked live) ~264
Step 3 of 3: checks every picked model and dataset id live on its hub, drops the ones that are not there, flags licenses, gating, staleness and low use, sizes each model against the compute (fits, a stretch that fits only quantized, or cloud, with who hosts it), marks picks the search didn't turn up as from memory, names every hub a pick is on, shows what it found that wasn't picked, and returns the report to show the user. If nextAction is SEARCH_FIRST or RECALL_REPLACEMENTS, do what its instructions say and call again.
| Name | Type | Req | Description |
|---|---|---|---|
| components | array | yes | – |
| compute | string | – | The hardware it has to run or train on, such as "one 24 GB GPU" or "CPU only". Size flags take the first "N GB" in it as the memory a model must fit in |
| need | string | yes | The ML need in plain words: what goes in, what should come out, and what it is for |
| recommendation | string | – | What to act on first: the one or two picks worth trying now and why (fit, license, use), then the ones to skip and why. Shown last, as "Start here" |
No output schema declared.
No examples provided.
How do I install the io.github.bikemeardsley/where-my-hug MCP server?
io.github.bikemeardsley/where-my-hug runs locally as an npm package, launched with npx -y where-my-hug. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · where-my-hug
claude mcp add bikemeardsley-where-my-hug -- npx -y where-my-hug
What is the io.github.bikemeardsley/where-my-hug MCP server?
io.github.bikemeardsley/where-my-hug is an MCP server listed in the public MCP registry as io.github.bikemeardsley/where-my-hug. Which parts of an ML need Hugging Face already covers. Every model and dataset checked live. This page covers its npm package (where-my-hug).
Is the io.github.bikemeardsley/where-my-hug MCP server safe to use?
io.github.bikemeardsley/where-my-hug scores 88 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 io.github.bikemeardsley/where-my-hug MCP server expose?
io.github.bikemeardsley/where-my-hug exposes 3 tools: where_my_hug, where_my_hug_search, where_my_hug_verify. Their descriptions and schemas cost roughly 665 tokens of context every time the server is loaded.
Is the io.github.bikemeardsley/where-my-hug MCP server still maintained?
io.github.bikemeardsley/where-my-hug 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 io.github.bikemeardsley/where-my-hug MCP server under?
io.github.bikemeardsley/where-my-hug declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.
What did VerifyMCP test on the io.github.bikemeardsley/where-my-hug MCP server?
VerifyMCP ran 16 checks on io.github.bikemeardsley/where-my-hug, covering Malware scan, Known CVEs, Install scripts, Dependency health and 12 more. Endpoint Security and Transport & Reachability do not apply to an npm package, so they were not tested. The score does not measure everything, and the scoring documentation lists what it leaves out.