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mcp-software-design

Scaffold a GoF pattern

scaffold_pattern

Create a language-agnostic pseudo-code skeleton for a GoF pattern, showing participants and their collaboration. Rename roles to your domain and translate to any language.

Instructions

Generate a language-agnostic pseudo-code skeleton for a GoF pattern, showing its participants and how they collaborate. Optionally rename roles to your domain (e.g. Product -> Notification). Translate the result into your target language.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
namesNoOptional role→name map, e.g. {"Product":"Notification","Creator":"Dispatcher"}.
patternYesPattern slug/name/alias, e.g. "observer", "factory-method".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okYes
codeNo
nameNo
slugNo
errorNo
rolesNo
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool generates a language-agnostic pseudo-code skeleton, shows participants and collaboration, and allows role renaming. It also clarifies that the output is not final code by saying 'Translate the result into your target language,' giving meaningful behavioral expectations. It does not discuss side effects or permissions, but for a generator these are not critical and the description is sufficiently transparent.

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 two sentences, front-loaded with the primary action, and every sentence serves a purpose. The second sentence expands on optional behavior without redundancy. It is concise and well-structured.

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?

Given the tool's complexity (2 params, output schema, nested objects), the description is complete. It explains the main function, optional customization, and the need for translation. The output schema covers return structure, so the description need not repeat it. No critical gaps are present.

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 coverage is 100%, so the schema fully documents both parameters. The description adds only a minor clarification ('Optionally rename roles to your domain') with an example already present in the schema. Thus it adds little meaning beyond the schema, supporting the baseline score of 3.

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: 'Generate a language-agnostic pseudo-code skeleton for a GoF pattern.' It specifies the action (generate), the resource (skeleton for a GoF pattern), and the content (participants and collaboration). This distinguishes it from sibling tools like list_catalog, explain_concept, and check_smells.

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?

The description implies usage for scaffolding a pattern and provides context about optional renaming and translation, making it clear when to use it. However, it does not explicitly mention alternatives or exclusions, such as using explain_concept for understanding rather than scaffolding. Thus it is clear context without explicit exclusion.

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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