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by faxik
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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reqs_stats

Count and analyze requirements by status, priority, section, or source to identify workload distribution and bottlenecks.

Instructions

Aggregated requirement counts by status x priority.

Args:

  • group_by: Group by: status, priority, section, source

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": "reqs_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
group_byNostatus

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description itself must convey behavior. 'Aggregated' strongly implies a read-only computation and not mutation, but it does not disclose scoping defaults, whether the aggregation covers all requirements, or how missing groups are represented. This is adequate but not rich.

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

Conciseness5/5

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

The description is two short items with no filler, and the core aggregation statement is front-loaded before the parameter notes. The Args section adds the one piece of non-schema information an agent needs.

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

Completeness4/5

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

The tool is simple: one optional parameter and an output schema exist, so return value details are covered elsewhere. The main gap is the absence of usage context, but that is already penalized in dimension 2; for a one-param stats tool this is otherwise complete.

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?

The schema provides only a title, default, and type for group_by, with 0% description coverage. The description compensates by listing the valid values: status, priority, section, source. It still leaves the meaning of section/source implicit, so it is not a 5.

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

Purpose4/5

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

The description states a clear operation and object: 'aggregated requirement counts' by status/priority. It is not vague or a tautology, but it does not distinguish reqs_stats from similar siblings like reqs_summary or stats, so it earns a 4 rather than a 5.

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?

No conditions or alternative tools are mentioned. An agent is left to infer that this is the right tool whenever requirement aggregates are needed, but there is no guidance on when a sibling like reqs_query or reqs_summary would be more appropriate.

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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