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io.github.neogeweb3/code-health-suite

io.github.neogeweb3/code-health-suite

by neogeweb3
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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check_naming

Detect PEP 8 naming violations in Python code. Scans files or directories to flag functions, methods, classes, and constants that don't follow snake_case, CamelCase, or UPPER_SNAKE_CASE, and provides corrective suggestions.

Instructions

Check Python naming conventions (PEP 8). Detects violations: functions/methods must be snake_case, classes must be CamelCase, constants must be UPPER_SNAKE_CASE. Returns violations with suggestions.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "check_naming"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesFile or directory path to check.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.8.1

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It specifies what the tool detects (three naming violation types) and what it returns (violations with suggestions), enough to imply a read-only static analysis operation.

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?

Three tight sentences: purpose, rule details, and return behavior. Every sentence adds value, and the key action is front-loaded.

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?

For a single-parameter, read-only linting tool with no output schema, the description explains the scope, accepted input concept, and return type. It could be more explicit about output shape, but the description is sufficiently complete for this complexity level.

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 only parameter, path, is fully described in the schema as 'File or directory path to check.' The tool description does not add further parameter semantics, so the baseline of 3 applies given 100% schema coverage.

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?

Description clearly states it checks Python naming conventions against PEP 8 and enumerates specific rule categories (snake_case, CamelCase, UPPER_SNAKE_CASE). This distinguishes it from sibling get_naming_score, which likely summarizes a score rather than returning detailed violations.

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?

Use case is implied: run this when you need to detect naming violations. However, no explicit mention of when not to use it or how it differs from get_naming_score, which is directly relevant among siblings.

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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