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FDEP MCP Server

FDEP MCP Server

by juspay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

group_similar_functions

Identify and group similar functions in Haskell codebases to reveal common patterns and reduce duplication through static analysis.

Instructions

Group functions by similarity to identify common patterns

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": "group_similar_functions"
  }
}

Input Schema

NameRequiredDescriptionDefault
similarity_thresholdNoMinimum similarity score to group functions
module_patternNoModule name pattern to filter analysis (optional)
min_group_sizeNoMinimum number of functions in a group
limitNoMaximum number of groups to return

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions grouping functions by similarity but doesn't explain what 'similarity' means (e.g., code structure, naming, dependencies), how groups are formed, whether this is a read-only analysis or modifies data, or what the output looks like. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.

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

Completeness2/5

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

Given the complexity of a similarity-based grouping tool with no annotations and no output schema, the description is insufficient. It doesn't explain the similarity metric, grouping methodology, output format, or performance considerations. For a tool that likely involves algorithmic analysis, more context is needed to use it effectively.

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%, so the schema already documents all four parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't clarify how 'similarity_threshold' relates to the grouping algorithm). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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: 'Group functions by similarity to identify common patterns.' This specifies the verb ('group'), resource ('functions'), and goal ('identify common patterns'). However, it doesn't explicitly differentiate from the sibling tool 'find_similar_functions,' which appears related but has a different purpose (finding vs. grouping).

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?

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'find_similar_functions' and 'find_code_patterns' that might overlap in functionality, there's no indication of when this grouping approach is preferred or what prerequisites might be needed.

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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