Server Quality Checklist

Tool Scores

• fashion_trend_analysisA
  4.4/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds behavioral details: returns specific output types, notes built-in data limitations for certain categories, and cost control via verbose/max_words. No contradictions.

  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?

  Concise at ~150 words, front-loaded with purpose, then details. Every sentence adds value. Well-structured with clear sections.

  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?

  With output schema present, description needn't detail return values. Covers main features: supported categories, output types, cost control, and usage context. Slightly lacking on how timeframe affects data, but adequate.

  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%, baseline 3. Description adds value by explaining verbose levels (quick insight, standard, detailed) and cost control. Also mentions default season detection and category limitations. Contributes beyond schema.

  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?

  Description clearly states 'Analyze current fashion trends for a specific product category' and lists specific outputs (keywords, colors, silhouettes, etc.). It mentions supported categories and distinguishes from sibling 'product_seo_audit' implicitly by focusing on trends.

  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?

  Explicit usage context: 'Use this when a fashion brand needs trend intelligence for content planning, product development, or seasonal merchandising strategy.' Also provides cost control guidance. No explicit when-not-to-use or alternatives, but clear enough.

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

• product_seo_auditA
  4.6/5.0
  Behavior5/5

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

  Annotations already indicate readOnly, idempotent, and non-destructive. The description adds valuable context: it checks multiple specific elements, returns a score per check and recommendations, and includes cost control details via verbose and max_words. No contradictions.

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

  Conciseness4/5

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

  The description is concise with 4 informative sentences, each serving a purpose. It is front-loaded with the main action. A slightly tighter structure could be possible, but it remains efficient.

  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 complexity (13 parameters, output schema exists), the description is thorough: it covers purpose, checks, usage context, return format, and cost control. Everything an agent needs is present.

  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%, so baseline is 3. The description adds value by explaining how `verbose` and `max_words` control output size and token cost, which goes beyond the schema definitions.

  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 audits a fashion product page for SEO best practices, listing specific checks (meta title, description, JSON-LD, etc.). It distinguishes from sibling 'fashion_trend_analysis' by focusing on SEO audit, providing a specific verb+resource.

  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?

  Explicitly states 'Use this whenever a fashion brand wants to optimize a product listing for organic search' and notes it works for any e-commerce platform. Does not include exclusion criteria, but the guidance is clear and sufficient.

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