Feel Lab
PYPI · FEELLAB-MCP
An FPS feel checklist, measured from a timeline any game's probe can write
Available components
Last scanned (16 hours ago)
Scores higher than 65% of PyPI servers
Category Testing and QA
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
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 28 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 1 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability55
- AI-judged instruction clarity (fair).Partial
- Tool/resource definitions use about 399 tokens (~66/item across 6 items; 6 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management0
- Stability not yet verified: not enough scan history yet (needs a 30-day window).Unverified
Tool Coverage78
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 33% 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 6 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (2026-07-28).Pass
Unverified: 1 category
A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.
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.
- 17 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 16 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 15 Oct 26 65
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 17 Oct 2026 · Analysed pypi/feellab-mcp@0.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 28 packages
| Packages resolved | 28 |
|---|---|
| No linked repository | 1 |
| 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 →
check_timeline ~37
A timeline file against the contract (feellab-timeline/1): every problem, or none.
| Name | Type | Req | Description |
|---|---|---|---|
| path | string | yes | – |
No output schema declared.
No examples provided.
gate_rows ~44
The metrics as rows for a gate (Weapon Lab's row format): subject, scene, metric, value.
| Name | Type | Req | Description |
|---|---|---|---|
| folder | string | yes | – |
| subject | string | yes | – |
No output schema declared.
No examples provided.
judge_folder ~63
Each subject's scenes judged against the checklist: every measured item's values, rules and verdict, a summary, and a Markdown report written beside them.
| Name | Type | Req | Description |
|---|---|---|---|
| folder | string | yes | a folder of <subject>_<scene>_timeline.json files |
| subject | – | – | – |
No output schema declared.
No examples provided.
list_checklist ~48
The checklist: every interaction, what the player should get, and how it is checked (measured or by a person).
| Name | Type | Req | Description |
|---|---|---|---|
| group | – | – | A to J, or leave out for all |
No output schema declared.
No examples provided.
list_metrics ~33
Every metric: its scene, unit, which way is better, its rule and the rule's source (estimate or evidence).
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
list_scenes ~28
The standard scenes a probe plays, one timeline each, and what each asks it to do.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
How do I install the Feel Lab MCP server?
Feel Lab runs locally as a PyPI package, launched with uvx feellab-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · feellab-mcp
claude mcp add hpdkhoa-feellab-mcp -- uvx feellab-mcp
What is the Feel Lab MCP server?
Feel Lab is an MCP server listed in the public MCP registry as io.github.hpdkhoa/feellab-mcp. An FPS feel checklist, measured from a timeline any game's probe can write. This page covers its PyPI package (feellab-mcp).
Is the Feel Lab MCP server safe to use?
Feel Lab scores 65 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 17 October 2026. 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 Feel Lab MCP server expose?
Feel Lab exposes 6 tools: list_checklist, list_scenes, list_metrics, check_timeline, judge_folder, gate_rows. Their descriptions and schemas cost roughly 253 tokens of context every time the server is loaded.
Is the Feel Lab MCP server still maintained?
Feel Lab is still listed as active in the MCP registry. We last reached this channel on 17 October 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 Feel Lab MCP server under?
Feel Lab declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.