Skip to main content
Glama

Generate an architecture compliance review checklist

generate_review_checklist

Generate a one-page Markdown review checklist to assess architecture compliance for a phase, deliverable, or project, turning criteria into send-back-if-missing checkpoints with conformance ratings.

Instructions

アーキテクチャ適合性レビュー用のチェックリストを Markdown のチェックボックス形式で生成する。フェーズ・成果物・実装プロジェクトのいずれかを対象に、知識ベースの記載項目や実務のコツを「これが満たされていなければ差し戻す」観点へ翻訳し、1 ページに収まる分量に絞る。判定は 準拠 / 一部準拠 / 非準拠 / 非適合 の 4 段階。 / Generate a one-page architecture compliance review checklist as Markdown checkboxes for a phase, a deliverable, or an implementation project. Knowledge-base content is translated into "send it back if this is missing" criteria, each rated conformant / partially conformant / non-conformant / irreconcilable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNo出力言語 / Output languageboth
scopeYesレビュー対象の種類 / What is being reviewed: an ADM phase, a deliverable, or an implementation project
targetNoフェーズ ID / 成果物 ID(implementation ではプロジェクト名などの自由記述)。省略時は現在のエンゲージメントのフェーズを使う / Phase id or deliverable id; free text for implementation. Defaults to the current engagement phase. — 最大 300 文字 at most 300 characters
Install Server

TDQS

A3.8/5.0
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 behavioral burden. It discloses the output format (Markdown checkboxes), the one-page length constraint, the 'send it back if missing' rule style, the four-level rating scheme, and the knowledge-base source of content. It does not explicitly state side effects, but the generation-focused language reasonably implies a non-mutating operation.

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

Conciseness3/5

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

The English portion is concise and front-loaded with purpose and output specifics. However, the Japanese version nearly duplicates the same information, adding length without adding distinct value for an AI agent evaluating the description, so not every sentence earns its place.

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 there is no output schema, the description does well by explaining what the result looks like: a one-page Markdown checklist with four possible ratings and criteria derived from knowledge-base content. It covers the main behaviors an agent needs to understand to invoke and interpret the tool, though it could be slightly more explicit about returned content structure.

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?

Input schema coverage is 100%: every parameter has a description, with enum values and defaults documented for lang and scope. The tool description adds little parameter-specific meaning beyond restating the scope options, but because the schema is already fully descriptive, the baseline of 3 is appropriate.

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 states a specific verb ('generate'), a specific resource ('architecture compliance review checklist'), and the exact scope options ('phase, deliverable, or implementation project'). It also specifies the output form ('Markdown checkboxes') and the review judgment scale, which clearly distinguishes it from siblings like generate_deliverable_template.

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 the tool is for architecture compliance review checklists and names the three target scope types, so the intended context is clear. However, it does not explicitly say when to prefer this tool over similar siblings such as security_requirements_checklist or generate_deliverable_template, and it offers no exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/Waganawa-Megumin/togaf10_EAP_MCP'

If you have feedback or need assistance with the MCP directory API, please join our Discord server