Skip to main content
Glama
Sign In
repo-guardian

repo-guardian

by prem-the-dev
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

find_dead_code

Detect unused Python imports, functions, and orphaned files in a project directory to eliminate dead code and improve maintainability.

Instructions

Detect unused Python imports, unused functions and orphaned files (e.g. files never imported or referenced).

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": "find_dead_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathNoPython package or project directory..

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

With no annotations, the description must carry the behavioral load. 'Detect' implies a read-only analysis, and the description enumerates what is searched for, but it does not explicitly state that no files are modified, how the scan traverses the path, or the nature of the returned results.

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?

A single front-loaded sentence that states the tool's core purpose immediately, with an inline clarifying example for 'orphaned files'. There is no filler or redundant restatement of the tool name.

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 one-optional-param read-only detector with no output schema, the description defines the scope and gives an illustrative example. It is complete enough for selection and invocation, though it stops short of describing the output format or limitations such as dynamic code detection.

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?

Schema coverage is 100% and the lone 'path' parameter is already described as a Python package or project directory. The description adds no extra parameter-level meaning, so the schema is doing the work; baseline 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 names a specific verb ('Detect') and a well-defined resource: unused Python imports, unused functions, and orphaned files with a clarifying example. This scope is clearly distinct from sibling tools like audit_dependencies and scan_for_secrets, so an agent can identify when this tool applies.

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?

Usage is implied: call this when you need to find dead code in a Python project. However, it does not explicitly state when to prefer it over the listed alternatives or mention any exclusions/preconditions, so guidance is adequate but not robust.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server

Other Tools