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AIWorkhub

AIWorkhub

by shrec
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

aiworkhub_manager_sdlc_outcome_metrics

Read-only

Read bounded outcome metrics derived from canonical task and review events, newest accepted tasks first, to track SDLC performance without modifying data.

Instructions

READ-ONLY: bounded outcome metrics from canonical task/review events.

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_sdlc_outcome_metrics"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum task-event rows read, newest accepted tasks first; default 500, clamped to 1-2000.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.12.67
    • addedInput schema / properties / limit / description
      Added value: +"Maximum task-event rows read, newest accepted tasks first; default 500, clamped to 1-2000."
  2. First observedv0.1.0

TDQS

C2.9/5.0
Behavior3/5

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

The annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds genuinely new context: the data source is 'canonical task/review events' and the read is 'bounded'. It does not disclose whether results are cached, whether the bound truncates aggregates (affecting correctness of totals), or any performance characteristics.

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?

One clause, front-loaded with the READ-ONLY qualifier, no wasted words. The terseness borders on under-specification rather than verbosity, but as a structure it is clean.

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

Completeness3/5

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

An output schema exists, so return shape need not be explained, and the single input is fully documented. What is missing is the substantive answer to 'metrics of what' — window, grouping, or unit — which for an analytics tool is the core question an agent would have before invoking it.

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 `limit` parameter is fully documented with default and clamping range, so the schema carries the load. The description's word 'bounded' loosely reinforces that cap but adds no syntax or behavioral nuance beyond the schema.

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

Purpose3/5

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

The description names the resource domain (canonical task/review events) and the kind of output (outcome metrics), so the agent knows it is an aggregation/reporting call rather than a mutation. But it never states which metrics are produced or what verb applies ('compute', 'aggregate'), so the purpose stays at the level of a hint rather than a definition.

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

Usage Guidelines2/5

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

There is no when-to-use guidance at all — no mention of the sibling SDLC tools (sdlc_case_get, sdlc_stage_packet, sdlc_case_for_task) that an agent might otherwise pick for per-case vs aggregate data. The agent must infer the selection rule from the tool name alone.

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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