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FDEP MCP Server

FDEP MCP Server

by juspay
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

analyze_function_complexity

Analyze Haskell function complexity metrics including call count and signature complexity to identify optimization opportunities in enterprise codebases.

Instructions

Analyze function complexity metrics including call count and signature complexity

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": "analyze_function_complexity"
  }
}

Input Schema

NameRequiredDescriptionDefault
module_nameNoModule to analyze (optional, analyzes all if not specified)
min_complexityNoMinimum complexity threshold
limitNoMaximum number of results

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 analyzing 'call count and signature complexity' but doesn't describe what the analysis entails, how results are returned, whether it's read-only or has side effects, or any constraints like performance or data limits. For a tool with zero annotation coverage, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary details. It's appropriately sized for the tool's complexity, with no wasted words. However, it could be slightly more structured by explicitly separating purpose from scope for better clarity.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on behavior, output format, and usage context. Without annotations or output schema, more completeness is needed to fully guide an agent, but it meets a basic threshold for a read-oriented analysis tool.

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 has 100% description coverage, with clear documentation for all three parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining 'call count' or 'signature complexity' in relation to the parameters. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to given the schema's completeness.

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: 'Analyze function complexity metrics including call count and signature complexity.' It specifies the verb ('analyze'), resource ('function complexity metrics'), and scope ('call count and signature complexity'). However, it doesn't explicitly differentiate from sibling tools like 'get_function_details' or 'get_most_called_functions' that might overlap in analyzing functions, missing full sibling distinction.

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 many sibling tools like 'get_most_called_functions' or 'get_function_details' that might handle similar analyses, there's no indication of context, prerequisites, or exclusions. Usage is implied only by the purpose, lacking explicit when/when-not statements.

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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