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mcp-software-design

mcp-software-design

by qwertymuzaffar
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TypeScript
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Explain a principle or pattern

explain_concept

Get an authoritative explanation of a design principle or GoF pattern: intent, when to use it, trade-offs, participants, and related concepts.

Instructions

Return an authoritative explanation of one design principle or GoF pattern: intent, when to use it, trade-offs, participants, and related concepts. Accepts a slug, full name, or alias (e.g. "SRP", "factory").

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": {
      "name": "<name>"
    },
    "name": "explain_concept"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYesConcept to explain, e.g. "open-closed", "SRP", "observer".

Output Schema

NameRequiredDescriptionDefault
nameNo
slugNo
foundYes
summaryNo
categoryNo
markdownNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It explicitly lists the output sections and input flexibility, making the behavior transparent. It does not detail edge cases or error handling, but these are minor for an explanation tool.

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?

Two sentences, front-loaded with the core action, no wasted words. Highly efficient and clear.

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

Completeness5/5

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

For a single-parameter tool with an output schema, the description fully covers what the tool does, what inputs are acceptable, and what the explanation will contain. No gaps remain for the agent to invoke correctly.

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?

Schema coverage is 100% for the single 'name' parameter. The description adds value by explaining accepted formats (slug, full name, alias) with examples, which goes beyond the schema's example string.

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?

Clearly states the tool returns an authoritative explanation of a design principle or GoF pattern, listing specific content sections (intent, when to use, trade-offs, participants, related concepts). This distinguishes it from siblings like scaffold_pattern (generation) and check_smells (analysis).

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

Usage Guidelines4/5

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

Provides clear context: use for explanations of design principles/patterns, accepts slug, full name, or alias. Does not explicitly exclude other tools, but the purpose is distinct enough for an agent to infer when to choose this tool over siblings.

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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