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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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audit_docstrings

Detect missing or low-quality Python docstrings across functions, methods, classes, and modules, returning per-entity coverage and quality issues.

Instructions

Audit Python docstring coverage and quality. Checks public functions, methods, classes, and modules for missing or low-quality docstrings. Returns per-entity coverage, missing docstrings, and quality issues.

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": "audit_docstrings"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesFile or directory path to audit.

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?

No annotations are provided, so the description carries the full behavioral burden. It transparently states what is checked (public functions, methods, classes, modules), what constitutes an issue (missing or low-quality docstrings), and what is returned (per-entity coverage, missing docstrings, quality issues). It does not define 'low-quality' criteria or edge cases, but it gives a solid behavioral overview.

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 sentences long, front-loaded with the tool's core purpose and immediately followed by what it returns. There is no filler or repetition of schema information.

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 tool with only one required parameter and no output schema, the description provides adequate contextual coverage: purpose, scope, and return content. It would be richer if it clarified how 'low-quality' is assessed or whether directory scans are recursive, but it is not incomplete for the tool's simplicity.

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 input schema as 'File or directory path to audit' (100% schema description coverage). The description adds no additional parameter-level semantics such as syntax, defaults, or examples, so the baseline score of 3 applies.

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 uses a specific verb ('Audit') with a clear resource ('Python docstring coverage and quality') and enumerates the exact entities checked: public functions, methods, classes, and modules. This differentiates it from the sibling get_docstring_score, which likely returns only a score rather than a detailed audit.

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 clearly implies that this tool should be used when you need docstring coverage and quality auditing, but it does not explicitly mention the closely related sibling get_docstring_score or state when to prefer one over the other. No alternative guidance is provided.

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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