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rush_complexity

Measures Python and JS/TS code complexity at a given path using radon or jscpd. If engines are missing, returns status='skipped'.

Instructions

Measure Python and JS/TS complexity at . Uses radon or jscpd; missing engines return status='skipped'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNo
toolNo
engineNo
statusNo
metricsNo
summaryNo
findingsNo
metadataNo
artifactsNo
duration_msNo
review_kindNo
engine_versionNo
review_providerNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description bears the full burden of behavioral disclosure. It usefully reveals reliance on external engines (radon/jscpd) and the 'skipped' status when they're missing. It does not state whether the operation is read-only or mention other side effects, but the mention of engine availability is meaningful.

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?

Two short sentences deliver the core action, the target languages, the location, and an important edge case. The main purpose is front-loaded and every word contributes, with no redundancy or filler.

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?

An output schema exists, so return values need not be described. The description covers the operation, languages, path, and missing-engine behavior. The only notable omissions are a precise definition of what metrics constitute 'complexity' and how engine selection works, but these are not required to invoke the tool correctly.

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?

The schema provides only a 'path' parameter with no description (0% coverage). The description adds that <path> is the location where complexity is measured, which provides basic semantic grounding. It does not specify whether the path should be a file or directory, leaving some ambiguity.

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?

The description states a specific verb (Measure), a specific resource (Python and JS/TS complexity), and a location parameter (<path>). This clearly differentiates it from sibling tools like rush_lint, rush_test, or rush_format by focusing on complexity analysis.

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 intended use is implied clearly: call this tool when you need complexity metrics for Python or JS/TS at a given path. It does not explicitly name alternative tools or when-not-to-use conditions, but the context is unambiguous enough for an agent to select it appropriately.

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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