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Mirdan

Mirdan

by S-Corkum
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

enhance_prompt

Improves developer prompts by adding quality requirements, codebase context, and tool recommendations to help AI coding assistants generate better structured code.

Instructions

Automatically enhance a coding prompt with quality requirements, codebase context, and tool recommendations.

Args: prompt: The original developer prompt task_type: Override auto-detection (generation|refactor|debug|review|test|planning|auto) context_level: How much context to gather (minimal|auto|comprehensive)

Returns: Enhanced prompt with quality requirements and tool recommendations

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": {
      "prompt": "<prompt>"
    },
    "name": "enhance_prompt"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYes
task_typeNoauto
context_levelNoauto

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses the tool's behavior by describing what it adds ('quality requirements, codebase context, and tool recommendations') and mentions auto-detection and context levels. However, it lacks details on permissions, rate limits, or error handling, leaving gaps for a tool with no annotation coverage.

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 appropriately sized and front-loaded, starting with a clear purpose statement. The Args and Returns sections are structured efficiently, with each sentence adding value without redundancy. It avoids unnecessary elaboration.

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?

Given the tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is fairly complete. It covers the tool's purpose, parameters, and return value. The output schema reduces the need to explain returns in detail, but more behavioral context could be added for a tool with no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate. It adds meaning by explaining each parameter's purpose: 'prompt' as the original input, 'task_type' with enum values and override function, and 'context_level' with options. This goes beyond the bare schema, though it could provide more detail on enum semantics.

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 clearly states the tool's purpose with specific verbs ('enhance a coding prompt') and resources ('quality requirements, codebase context, and tool recommendations'). It distinguishes from siblings like 'analyze_intent' or 'get_quality_standards' by focusing on prompt enhancement rather than analysis or standards retrieval.

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?

The description implies usage through the mention of 'auto-detection' and parameter defaults, but does not explicitly state when to use this tool versus alternatives like 'suggest_tools' or 'validate_code_quality'. It provides some context but lacks clear exclusions or named alternatives.

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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