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by lesleslie
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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detect_clones

Destructive

Identify duplicate code segments in Python files using APTED tree edit distance and LSH acceleration to maintain code quality and reduce redundancy.

Instructions

Detect code clones using APTED tree edit distance and LSH acceleration

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": "detect_clones"
  }
}

Input Schema

NameRequiredDescriptionDefault
group_clonesNoGroup related clones together (default: true)
min_linesNoMinimum lines to consider as clone (default: 5)
pathYesPath to Python code to analyze
similarity_thresholdNoMinimum similarity threshold 0.0-1.0 (default: 0.8)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.14.3

TDQS

B3.3/5.0
Behavior3/5

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

Annotations indicate this is a destructive, non-idempotent, non-read-only operation with open-world data. The description adds value by specifying the algorithms used (APTED and LSH), which helps the agent understand computational behavior, but doesn't elaborate on side effects, rate limits, or output format beyond what annotations imply.

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, efficient sentence with zero waste. It front-loads the core purpose ('Detect code clones') and adds technical details (algorithms) that are relevant for agent understanding, making it appropriately sized and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (destructive analysis with multiple parameters) and lack of output schema, the description is minimal. It covers the purpose and methods but omits details on output format, error handling, or performance considerations, leaving gaps for the agent to navigate.

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 description coverage is 100%, with clear parameter descriptions in the schema. The description adds no additional parameter semantics beyond implying analysis of Python code via 'path', which is already covered. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Detect code clones' using specific algorithms (APTED tree edit distance and LSH acceleration). It specifies the resource (Python code) and method, but doesn't explicitly differentiate from sibling tools like 'find_dead_code' or 'analyze_code' beyond the clone detection focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'find_dead_code' or 'analyze_code'. The description lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and purpose.

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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