Server Quality Checklist

Tool Scores

• analyze_uiC
  2.6/5.0
  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 mentions the action 'analyze' and outcome 'provide improvement recommendations', but lacks details on permissions, rate limits, output format, or whether it's read-only or mutative. This is inadequate for a tool with no annotation coverage.

  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 a single, efficient sentence that front-loads the core purpose. It avoids redundancy and waste, though it could be slightly more structured by separating analysis from recommendations. Overall, it's appropriately sized for the tool's complexity.

  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 no annotations, no output schema, and 4 parameters, the description is incomplete. It doesn't explain what the recommendations look like, how they're generated, or any behavioral traits. For a tool that likely involves analysis and suggestions, more context is needed to guide the agent 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%, so the schema already documents all four parameters with descriptions. The description adds no additional meaning or context beyond what the schema provides, such as how parameters interact or examples of usage. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 states the tool's purpose as analyzing UI/UX and providing recommendations, which is clear but vague. It specifies the verb 'analyze' and resource 'current UI/UX' with the outcome 'improvement recommendations', but doesn't differentiate from sibling tools like 'improve_component' or 'generate_design_tokens', leaving ambiguity about scope.

  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. With sibling tools like 'improve_component' and 'generate_design_tokens', there's no indication of context, prerequisites, or exclusions, leaving the agent to guess based on tool names alone.

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

• generate_design_tokensC
  2.6/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. 'Generate' implies a creation operation, but the description doesn't disclose whether this creates persistent resources, requires authentication, has rate limits, or what format the output takes. No behavioral traits beyond the basic action are described.

  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 a single sentence with zero waste - 'Generate a comprehensive design token system' is front-loaded and efficient. However, it's arguably too concise given the lack of behavioral context and usage guidance needed for a generation tool with no annotations.

  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?

  For a generation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what a 'design token system' entails, what format it returns, whether it's persistent, or how it relates to sibling tools. The 100% schema coverage helps with parameters, but overall context is incomplete for effective tool selection.

  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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain relationships between parameters or how they affect the generated tokens. Baseline 3 is appropriate when schema does the heavy lifting.

  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 'Generate a comprehensive design token system' states what the tool does (generate design tokens) but is somewhat vague about scope and output. It doesn't specify what constitutes 'comprehensive' or how this differs from sibling tools like create_component or improve_component. The verb+resource is clear but lacks specificity about the generated artifact.

  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. There's no mention of when this tool is appropriate versus analyze_ui, create_component, or improve_component. No context about prerequisites, typical use cases, or exclusions is provided.

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

• improve_componentC
  2.6/5.0
  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. 'Improve' implies mutation, but the description doesn't specify whether this is a destructive operation, what permissions are needed, whether changes are reversible, or what the output looks like. It mentions 'best practices' but doesn't explain what those entail or how they're applied.

  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 a single, efficient sentence that gets straight to the point. There's no wasted language or unnecessary elaboration. However, it could be more front-loaded with critical information about what type of improvement occurs.

  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?

  For a mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'improve' means operationally, what the output looks like, or what happens to the original component. The agent must guess whether this returns modified code, suggestions, or something else entirely.

  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 four parameters thoroughly. The description adds no additional meaning beyond what's in the schema - it doesn't explain how parameters interact, provide examples of 'improvements', or clarify the relationship between 'framework' and 'best practices'. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 states the tool's purpose ('Improve existing UI component') and mentions 'best practices', which provides some context. However, it's vague about what 'improve' entails and doesn't clearly differentiate from sibling tools like 'analyze_ui' or 'create_component'. It doesn't specify whether this is about code refactoring, performance optimization, or design enhancement.

  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. There's no mention of prerequisites, when not to use it, or how it differs from sibling tools like 'analyze_ui' (which might analyze without improving) or 'create_component' (which creates new components). The agent must infer usage from the tool name alone.

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

• create_componentC
  2.9/5.0
  Behavior2/5

  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 states this is a creation tool but doesn't address permissions needed, whether it's idempotent, what happens on failure, or any rate limits. 'Modern best practices' is vague and doesn't clarify behavioral traits.

  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 wasted words. It's appropriately sized and front-loaded with the core purpose, making it easy to scan and understand quickly.

  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?

  For a creation tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'create' entails (e.g., file generation, API call), what the return value might be, or how 'modern best practices' influences behavior, leaving significant gaps 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 5 parameters. The description adds no additional meaning about parameters beyond implying 'modern best practices' might relate to them, but this is too vague to provide real value over the schema.

  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 verb ('create') and resource ('new UI component'), and specifies 'with modern best practices' adds some differentiation. However, it doesn't explicitly distinguish this from sibling tools like 'improve_component' or 'analyze_ui', which prevents a perfect score.

  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 like 'improve_component' or 'generate_design_tokens'. It mentions 'modern best practices' but doesn't specify contexts, prerequisites, or exclusions for usage.

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