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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_skill_usage

Read-only

Read per-skill usage statistics: version counts, accepted and negative evidence rows, distinct actor identities, injection receipts, and the exact reason a skill cannot be injected. Read-only.

Instructions

MANAGER READ: per-skill usage statistics, measured from the stored record.

For every stored skill version: how many versions of that identity are proposed, its evidence rows split by outcome (accepted/negative), the number of CANONICAL independent actor identities its accepted evidence rests on (not the raw strings -- two spellings of one manager count once), the cards it was injected into according to the persisted selection receipts, whether it is injectable at all, and when it is not, the exact named reason:

lifecycle_state_is_retired unresolved_negative_evidence activation_evidence_below_two_distinct_actors lifecycle_state_is_proposed_not_active

Read-only. It never activates, retires or evidences anything.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "aiworkhub_manager_skill_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
min_accepted_evidenceNoAccepted-evidence threshold, default 2; it is only echoed back in the report and does not change any per-skill figure.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.67
    • addedInput schema / properties / min_accepted_evidence / description
      Added value: +"Accepted-evidence threshold, default 2; it is only echoed back in the report and does not change any per-skill figure."
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only supply readOnlyHint and openWorldHint; the description adds real depth beyond that: it is computed from persisted state, canonicalizes actor identities ('two spellings of one manager count once'), derives injection counts from persisted selection receipts, and enumerates the four exact non-injectable reason codes. It also explicitly disclaims side effects (never activates, retires or evidences).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the identity ('MANAGER READ: per-skill usage statistics') before elaborating, and each sentence carries information. The indented reason-code list is long but functions as an interpretation key for results, though with an output schema present part of it is arguably redundant.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-required-parameter read tool with an output schema and readOnly annotations, the description covers source of truth, computed semantics and failure reasons. An agent has everything needed to call it and to interpret the special non-injectable cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single optional parameter already documents that min_accepted_evidence is echoed back only and does not alter per-skill figures. The description adds nothing about parameters, so the baseline of 3 applies - the schema does all the work.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('per-skill usage statistics, measured from the stored record') and then enumerates exactly what dimensions are reported: version counts, evidence by outcome, canonical actor counts, injection receipts, injectability. The closing 'Read-only. It never activates, retires or evidences anything' cleanly separates it from the write-side siblings skill_activate / skill_add_evidence / skill_propose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied rather than stated: an agent can infer 'call this to audit skill usage' from the read-only framing. However, no near-neighbour is named - notably skill_retirement_report, skill_evidence_sweep and recipe_usage are plausibly overlapping and the description gives no rule for choosing between them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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