io.github.derekchoyai/hone
MCPB · HONE.MCPB · SCANNED AUG 3
Score your judgment over AI-assisted work. Hone scores you, not the model.
Available components
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 →
Supply Chain Security13
- Malware scan not yet available for this package.Unverified
- CVE data not yet available for this package.Unverified
- No install/post-install scripts declared.Pass
- Only part of the dependency tree could be resolved (95 of 100), so this covers what we could see, not the whole tree. 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 (Apache-2.0).Pass
- Actively maintained (last published 43 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability29
- AI-judged instruction clarity (poor).Fail
- Context-footprint check failed: tool/resource definitions use about 991 tokens (~247/item across 4 items; 4 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 Management27
- Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage86
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 59% of tool parameters carry a description.Partial
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.
mcpb · hone.mcpb
Download bundleEvery 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.
- 2 Aug 26 +13
- Maintenance: unverified → pass ▲ functional
- Dependency health: unverified → partial ▲ functional
- Stability: unverified → 0.23 ▲ functional
- MCP protocol: unverified → pass ▲ functional
- Schema quality: unverified → poor ▲ functional
- 1 Aug 26 +10
- Dependency health: partial → unverified ▼ functional
- Tool coverage: unverified → 100 ▲ functional
- 31 Jul 26 −19
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 30 Jul 26 +2
- Maintenance: unverified → pass ▲ functional
- 28 Jul 26 −2
- Maintenance: pass → unverified ▼ functional
- 27 Jul 26 32
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 3 Aug 2026 · Analysed mcpb/https://github.com/derekchoyai/hone/releases/download/v0.3.0/[email protected]
Provenance none
Ecosystem: mcpb · Outcome: none
Dependencies 95 packages
95 packages in the resolved dependency tree · 95 deprecated · 29 stale.
The dependency tree was only partially resolved, so these counts may be incomplete.
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.
get_profile ~92
The user's longitudinal AI-Q profile from this machine's local history (~/.hone/profile.json): review count, per-dimension Discernment averages, weakest/strongest dimension, composite and calibration trends, Delegation stats when assessed, qualitative Design indicators, and recent reviews. Use it to answer 'how is my judgment developing?' or to ground coaching in their actual history. Contains judgment metadata only — never any reviewed work.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_protocol ~279
Get the open AI-Q standard protocol for computing the user's AI-Q (their judgment over AI-assisted work — the three Ds: Discernment, Delegation, Design) in YOUR context. mode='after' (default) reviews FINISHED AI-assisted work: dimension definitions, domain rubric, interview rules (incl. the delegation probe), question count, techniques, deterministic scoring, and the report + teaching (3-5 lessons) structure. mode='during' is THINK-FIRST — Delegation coaching: the user has not produced anything yet; sharpen their prompt and prime their judgment BEFORE the AI answers (no Discernment score exists yet). You (the host model) already have the work/task loaded — you do the reading and run the interview; this tool supplies the methodology. Call this BEFORE interviewing the human.
| Name | Type | Req | Description |
|---|---|---|---|
| context | string | — | Who/where: adapts language, tone, depth, and the scoring bar. |
| domain | string | yes | The kind of work being reviewed (or the task domain in think-first mode). |
| mode | string | — | 'after' = review finished work (default, scored). 'during' = think-first: prime judgment before using AI (not scored). |
| riskLevel | string | — | low = throwaway; medium = shared/internal; high = production, money, health, legal, irreversible. |
No output schema declared.
No examples provided.
record_review ~335
Save a completed judgment review to the user's LOCAL profile (~/.hone/profile.json) — judgment METADATA ONLY (scores, domain, a short subject label), never the work itself. Pass the six Discernment dimensions, plus the three Delegation facets when assessed; a think-first delegation read passes just the facets. All sub-scores and the AI-Q composite are recomputed server-side (passed composites are ignored — anti-gaming). Future get_protocol calls personalize from this history. Returns the updated trend and an encouragement read. Tell the user you're saving scores only, and skip this call if they decline.
| Name | Type | Req | Description |
|---|---|---|---|
| accountability | — | — | — |
| assumptionAwareness | — | — | — |
| briefQuality | — | — | 1-5 — Delegation facet; pass ONLY when assessed behind the evidence gate. |
| confidenceCalibration | — | — | — |
| context | string | — | — |
| domain | string | yes | The kind of work that was reviewed. |
| iterationControl | — | — | 1-5 — Delegation facet; pass ONLY when assessed. |
| riskLevel | string | — | — |
| riskRecognition | — | — | — |
| statedConfidence | number | — | The 0-100 confidence the human stated BEFORE the reveal. |
| subject | string | — | ≤8-word neutral label of WHAT was reviewed (e.g. 'Q3 GTM strategy deck') — a recall anchor, never the content. |
| taskSelection | — | — | 1-5 — Delegation facet; pass ONLY when assessed. |
| topGap | string | — | One short sentence: the single most important gap from this review. |
| understanding | number | — | 1-5 |
| verification | — | — | — |
No output schema declared.
No examples provided.
score_review ~285
Deterministically score a completed AI-Q judgment interview into the user's AI-Q. Pass the six 1-5 Discernment dimension scores (judged by comparing the human's answers to your private work map), and — ONLY if your interview surfaced the brief/iteration history — the three 1-5 Delegation facets. Returns the official AI-Q composite (0-100, weights renormalized over what was assessed), band, sub-scores, and calibration read. For a think-first delegation-only read, pass just the three facets. ALWAYS use this instead of computing any score yourself.
| Name | Type | Req | Description |
|---|---|---|---|
| accountability | — | — | — |
| assumptionAwareness | — | — | — |
| briefQuality | — | — | 1-5 — Delegation: did the brief carry the context, constraints, and success criteria the task needed? Pass ONLY with evidence from the interview. |
| confidenceCalibration | — | — | — |
| iterationControl | — | — | 1-5 — Delegation: caught and corrected drift between first output and final. Pass ONLY with evidence. |
| riskRecognition | — | — | — |
| statedConfidence | number | — | The 0-100 confidence the human stated BEFORE seeing any results. |
| taskSelection | — | — | 1-5 — Delegation: right task to hand to AI; knew what to keep human. Pass ONLY with evidence. |
| understanding | number | — | 1-5 |
| verification | — | — | — |
No output schema declared.
No examples provided.