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Anselmoo

mcp-server-analyzer

by Anselmoo

analyze-code

Read-onlyIdempotent

Analyze Python code with Ruff, ty, and Vulture to detect linting issues, type errors, and dead code for comprehensive code quality improvement.

Instructions

Comprehensive analysis combining Ruff linting, ty type checking, and Vulture dead code detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesPython code to analyze
ruff_config_pathNoOptional path to RUFF configuration file
min_confidenceNoMinimum confidence level for VULTURE (default: 80)
project_pathNoOptional project directory used by ty for config and import resolution

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
ruff_resultYesRUFF linting results
ty_resultYesty type-checking results
vulture_resultYesVULTURE dead code detection results
summaryYesSummary statistics of the analysis
Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, reducing the burden. The description adds that it combines three tools, but no additional behavioral details like performance or error handling are disclosed.

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 a single concise sentence that front-loads the purpose with no unnecessary words or repetition.

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?

Given the output schema exists and annotations are present, the description is nearly complete. It lacks usage guidance for when to use this combined tool rather than individual ones, but covers purpose and scope adequately.

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?

All parameters have schema descriptions (100% coverage) with clear roles for code, ruff_config_path, min_confidence, and project_path. The description adds no extra meaning beyond 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?

The description clearly states it performs comprehensive analysis combining Ruff, ty, and Vulture, with specific verb 'analyze' and resource 'code'. It distinguishes itself from sibling tools that focus on single tools.

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?

The description does not explicitly state when to use this combined tool versus individual siblings like ruff-check or ty-check. It implies comprehensive analysis but lacks direct alternatives or context.

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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