Majin Slide MCP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have completely distinct and non-overlapping purposes: one generates a prompt for creating slides, while the other saves generated slides to a file. There is no ambiguity or confusion between them, as they serve different stages in the slide creation workflow.
Naming Consistency5/5Both tools follow a consistent verb_noun naming pattern (generate_slide_prompt and create_slide_file), using snake_case and clear action-object pairs. This consistency makes the tools easily understandable and predictable in their naming.
Tool Count2/5With only two tools, the server feels under-scoped for a slide creation domain, as it lacks essential operations like editing, deleting, or listing slides. While the tools cover prompt generation and file saving, the overall functionality is thin and incomplete for typical presentation workflows.
Completeness2/5The server is severely incomplete for slide creation, missing core CRUD operations such as updating or deleting slides, and lacking tools for managing slide content or metadata. The two tools only address initial generation and saving, leaving significant gaps that will hinder agent effectiveness in handling full slide lifecycle tasks.
Average 3.7/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- 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 of behavioral disclosure. It reveals several behavioral traits: the tool generates prompts (not actual slides), assumes a default of 10 slides ('既定で10枚想定'), and produces text that can be directly used as model instructions ('返却テキストはモデルへの指示文としてそのまま利用できます'). However, it doesn't disclose potential limitations, error conditions, or whether the output is deterministic versus creative.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two sentences. The first sentence states the core functionality, and the second provides usage guidance. Both sentences earn their place by adding value. However, it could be slightly more front-loaded by making the default 10-slide assumption more prominent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a single parameter tool with no annotations and no output schema, the description provides adequate but incomplete context. It explains what the tool does and when to use it, but doesn't describe the output format beyond 'model instructions', doesn't mention potential errors or constraints, and doesn't differentiate from the sibling tool. For a generation tool with no output schema, more detail about the return value would be helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the single parameter 'topic' well-documented as 'The main topic or subject of the presentation'. The description adds minimal parameter semantics beyond the schema, mentioning only that prompts are generated 'from topics' without providing additional context about topic format, length constraints, or examples. With high schema coverage, the baseline is 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Marp用のプレゼン資料プロンプトをトピックから生成します' (generates Marp presentation prompts from topics). It specifies the verb ('生成します' - generates), resource ('プレゼン資料プロンプト' - presentation prompts), and target format ('Marp用' - for Marp). However, it doesn't explicitly differentiate from its sibling 'create_slide_file', which appears to create actual slide files rather than prompts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: 'ユーザーが“スライドを作って/説明して/要点をまとめて”など資料化やプロジェクト・機能説明を求めた場合に積極的に実行してください' (actively execute when users request slide creation, explanations, summarization of key points, or project/feature explanations). It gives specific trigger phrases and use cases. However, it doesn't explicitly state when NOT to use it or mention the sibling alternative 'create_slide_file' as an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- 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 key behavioral traits: it saves files, overwrites existing ones with the same name, and should be used after model output. However, it lacks details on permissions needed, error handling, or file system impacts. The description doesn't contradict annotations (none exist).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded: two sentences that directly state the purpose and usage guidelines. Every sentence earns its place with no wasted words. The structure is logical: what it does, followed by when/how to use it.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (file creation with overwrite behavior), no annotations, and no output schema, the description is partially complete. It covers the core action and timing but lacks details on return values, error cases, or system dependencies. It's adequate for basic use but has gaps for robust agent operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents parameters (filename, content, output_dir). The description adds no additional parameter semantics beyond what's in the schema. It implies parameters through context (e.g., 'Markdownスライド' relates to content, 'ファイル' to filename) but doesn't explain them explicitly. Baseline is 3 when schema coverage is high.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '生成済みのMarkdownスライドを.mdファイルとして保存します' (saves generated Markdown slides as .md files). It specifies the verb (保存/save) and resource (Markdown slides as .md files). However, it doesn't explicitly differentiate from the sibling tool 'generate_slide_prompt', which likely generates slides rather than saving them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use it: 'モデルがMarkdownを出力した直後に続けて実行してください' (execute immediately after the model outputs Markdown). It also mentions behavior with existing files: '同名があれば上書き' (overwrites if same name exists). However, it doesn't explicitly state when NOT to use it or name alternatives like the sibling tool.
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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