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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_recipe_usage

Read-only

Reports which registered recipes actually run, with run counts, distinct verified actors, first and last run, exit-code spread and bytes returned. Spot recipes registered but never run.

Instructions

MANAGER READ: which registered recipes are actually used, and by whom.

aiworkhub_manager_recipe_list answers how many recipes EXIST. This answers whether anything uses them: every registered recipe appears with its run count, how many DISTINCT verified actors ran it, its first and last run, its exit-code distribution and the bytes those runs returned -- and a recipe with no runs appears with used false rather than being omitted, because "registered and never run" is the actionable state.

The actor on every receipt is derived by the server from the verified route, never from a caller-supplied string, so distinct_actors cannot be inflated by typing a new name. Runs recorded before actors existed are reported as unattributed_runs rather than credited to anyone.

since is an ISO-8601 UTC timestamp bounding the evidence window; registered_count still covers every stored manifest, and window_only marks the asymmetry.

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_recipe_usage"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum recipes to return; default 100, clamped to 1-1000.
sinceNoISO-8601 UTC timestamp bounding the run evidence to runs from that time onward; registered_count still covers every stored recipe. Omit to count runs of all time.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv0.12.67
    • addedInput schema / properties / limit / description
      Added value: +"Maximum recipes to return; default 100, clamped to 1-1000."
    • addedInput schema / properties / since / description
      Added value: +"ISO-8601 UTC timestamp bounding the run evidence to runs from that time onward; registered_count still covers every stored recipe. Omit to count runs of all time."
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only declare readOnlyHint/openWorldHint, so the description carries the real disclosure burden and does so: zero-run recipes are returned with used=false rather than omitted, actors are server-derived from the verified route and cannot be inflated, and pre-actor history is surfaced as unattributed_runs instead of being credited. These are non-obvious semantic guarantees that materially change how an agent should interpret the result.

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 a clear scope line and organized as purpose → differentiation → data guarantees → parameter caveat. Some phrasing is essayistic ('because "registered and never run" is the actionable state'), but each sentence still carries a distinct, load-bearing fact.

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?

An output schema exists so return typing needn't be restated, yet the description still explains the interpretation-critical fields (used, distinct_actors, unattributed_runs, window_only). With both parameters and the empty-result behavior accounted for, nothing an agent needs to call and read this correctly is missing.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3, but the description adds genuine meaning for `since`: it bounds only the run evidence while `registered_count` still covers every stored manifest, and `window_only` marks that asymmetry. That interpretation is not derivable from the schema text alone; only `limit` is left to the schema.

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?

Opening line gives an explicit verb+resource scope ('which registered recipes are actually used, and by whom') and immediately distinguishes it from the sibling that answers the adjacent question ('recipe_list answers how many recipes EXIST'). An agent can route between the two without opening either schema.

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

Usage Guidelines4/5

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

The description names the alternative (recipe_list) and the condition that selects this one (utilization rather than existence), which is a clear routing rule. It falls short of 5 only because it does not address when not to use it relative to the other recipe siblings (recipe_show, recipe_run, skill_usage).

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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