Server Quality Checklist

Tool Scores

• get_visually_similarC
  2.7/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get visually similar products' implies a read operation, but it doesn't disclose details like whether this is a search or recommendation system, potential rate limits, authentication needs, or what the return format might be. The description is too minimal to provide meaningful behavioral context beyond the basic action.

  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 a single, efficient sentence with zero waste—'Get visually similar products' is front-loaded and to the point. Every word earns its place, making it highly concise and well-structured for quick understanding, though this brevity contributes to gaps in other dimensions.

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

  Completeness2/5

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

  Given the complexity of 8 parameters, no annotations, and no output schema, the description is incomplete. It fails to explain the tool's domain (e.g., e-commerce, fashion), how results are returned, or key behavioral aspects. For a tool with rich parameters but minimal description, this leaves significant gaps in understanding for an AI agent.

  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 description coverage is 100%, so the schema already documents all 8 parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining how 'product_id' relates to visual similarity or how parameters interact. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose3/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description 'Get visually similar products' clearly states the verb ('Get') and resource ('visually similar products'), but it's vague about scope and doesn't distinguish from sibling tools like 'get_complete_the_look' or 'get_style_guide'. It doesn't specify whether this is for fashion, retail, or another domain, leaving the purpose somewhat ambiguous despite having a clear basic action.

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

  Usage Guidelines2/5

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

  No guidance is provided on when to use this tool versus alternatives like 'get_complete_the_look' or 'get_style_guide'. The description lacks context about scenarios (e.g., for product recommendations, visual search) or prerequisites, leaving the agent to infer usage based on the tool name alone without explicit direction.

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

• get_complete_the_lookC
  2.9/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('Get outfit recommendations') without mentioning any behavioral traits such as rate limits, authentication needs, response format, or potential side effects. For a tool with 9 parameters and no annotations, this is a significant gap in transparency.

  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 a single, clear sentence: 'Get outfit recommendations for a product.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by directly stating the tool's function.

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

  Completeness2/5

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

  Given the tool's complexity (9 parameters, no annotations, no output schema), the description is incomplete. It lacks information on behavioral traits, usage guidelines, and output details, which are crucial for an AI agent to invoke the tool correctly. The description alone is insufficient to provide a full understanding of how to use this tool effectively.

  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 description coverage is 100%, meaning all parameters are documented in the input schema. The description adds no additional meaning beyond the schema, as it doesn't explain parameter interactions, default behaviors, or usage examples. With high schema coverage, the baseline score is 3, reflecting that the description doesn't compensate but also doesn't detract from the schema's documentation.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get outfit recommendations for a product.' It specifies the verb ('Get') and resource ('outfit recommendations'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_style_guide' or 'get_visually_similar,' which would require more specific language about the type of recommendations provided.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_style_guide' or 'get_visually_similar,' nor does it specify scenarios or prerequisites for usage. This lack of context leaves the agent to infer usage based on the tool name alone, which is insufficient for optimal selection.

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

• get_style_guideB
  3.1/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides 'advice and tips,' implying a read-only, informational function, but doesn't clarify aspects like whether it requires authentication, has rate limits, or returns structured data. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

  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 a single, efficient sentence: 'Get styling advice and tips for creating effective fashion recommendations.' It's front-loaded with the core purpose and avoids unnecessary words, making it easy to parse quickly.

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

  Completeness3/5

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

  Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on output format, error handling, or integration with sibling tools. Without an output schema, the description should ideally hint at return values, but it doesn't, leaving room for improvement in completeness.

  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 description coverage is 100%, with clear descriptions for all three parameters (category, fashion_season, occasion). The description doesn't add any extra meaning beyond the schema, such as explaining how parameters interact or providing usage examples. Since the schema already documents parameters well, the baseline score of 3 is appropriate.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Get styling advice and tips for creating effective fashion recommendations.' It specifies the verb 'Get' and the resource 'styling advice and tips,' making the function understandable. However, it doesn't differentiate from sibling tools like 'get_complete_the_look' or 'get_visually_similar,' which likely offer different types of fashion-related outputs, so it misses full distinction.

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

  Usage Guidelines2/5

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

  The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools or specify contexts where this tool is preferred, such as for general advice versus specific outfit generation. Without any usage context, the agent lacks direction on tool selection.

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