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repo-guardian

repo-guardian

by prem-the-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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audit_dependencies

Audit Python dependencies to uncover outdated, missing, or fixed-version packages by scanning pyproject.toml, requirements files, and setup.py/setup.cfg.

Instructions

Audit Python project dependencies for outdated, missing or pinned-to-fixed-version packages. Reads pyproject.toml, requirements*.txt and setup.py/setup.cfg.

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": "audit_dependencies"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNoRepository or project root directory..

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose that the tool 'Reads' specific manifest files, which strongly implies a non-mutating operation. However, it does not mention whether the tool makes network calls to check for outdated versions, what side effects may occur, or how results are returned.

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 efficient sentences with no filler. The primary audit purpose is front-loaded, followed by concrete file-format details, and every clause adds useful 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 one optional parameter and no output schema, the description is nearly complete: it defines the audit scope, names the file types inspected, and implies a read-only operation. The main gap is not describing the result format or whether it consults an external package index, but the tool remains straightforward to invoke correctly with just a path.

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 input schema already documents the single 'path' parameter as 'Repository or project root directory' with 100% coverage, so the description adds little parameter-level meaning. It reinforces that the path points to a Python project root, but this is not a significant addition 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 states a specific verb ('audit'), a clear resource ('Python project dependencies'), and the exact conditions it checks (outdated, missing, or pinned-to-fixed-version packages). It also names the manifest files it reads, making it easy to distinguish from sibling tools like check_licenses, scan_for_secrets, and find_dead_code.

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 description clearly signals when to use the tool: when auditing Python project dependency health. The scope is explicit enough that an agent can tell it apart from license, secrets, and dead-code tools, but it does not explicitly name alternatives or state exclusions.

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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