Server Quality Checklist

Tool Scores

• boost_promptB
  3.3/5.0
  Behavior3/5

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

  Annotations indicate readOnlyHint=true (safe operation), openWorldHint=true (handles diverse inputs), and idempotentHint=false (non-idempotent). The description adds value by specifying the transformation is for 'clarity and effectiveness' and involves 'prompt engineering best practices', which provides behavioral context beyond annotations. However, it doesn't detail aspects like rate limits, error handling, or output format, keeping the score moderate.

  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 that front-loads the core action ('Transform a prompt') and adds necessary context without waste. Every word contributes to understanding the tool's purpose, making it appropriately sized and well-structured.

  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 (transformation operation), annotations cover safety and input handling, but there's no output schema to explain return values. The description adequately states the purpose but lacks details on usage guidelines, behavioral nuances like transformation specifics, or how it differs from siblings, leaving gaps in completeness 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?

  The input schema has 100% description coverage, with the 'prompt' parameter well-documented as 'Prompt to transform and optimize'. The description adds marginal meaning by implying optimization for 'clarity and effectiveness', but it doesn't provide additional syntax, examples, or constraints beyond the schema. Baseline 3 is appropriate given high schema coverage.

  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 with a specific verb ('Transform') and resource ('prompt'), and it adds context about 'prompt engineering best practices' and goals like 'maximum clarity and effectiveness'. However, it doesn't explicitly differentiate from sibling tools like 'crafting_prompt' or 'fix_prompt', which might have overlapping or distinct functions, preventing 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 such as 'crafting_prompt' or 'fix_prompt'. It implies usage for optimizing prompts but lacks explicit when/when-not scenarios, prerequisites, or comparisons to siblings, leaving the agent with minimal context for selection.

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

• fix_promptB
  3.3/5.0
  Behavior3/5

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

  Annotations indicate readOnlyHint=true (safe read operation), openWorldHint=true (broad applicability), and idempotentHint=false (non-idempotent). The description adds context by specifying the refinement goals (clarity, readability, flow), which goes beyond the annotations. However, it does not disclose other behavioral traits like potential side effects, rate limits, or detailed output expectations, keeping the score moderate.

  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 that front-loads the core action ('polish and refine') and purpose. It avoids redundancy and wastes no words, making it highly concise and well-structured for quick understanding.

  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 (single parameter, no output schema), the description is adequate but has gaps. It explains what the tool does but lacks details on when to use it versus siblings, output format, or error handling. With annotations covering safety and scope, it meets minimum viability but isn't fully comprehensive.

  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?

  The input schema has 100% description coverage, fully documenting the single parameter 'prompt'. The description does not add any parameter-specific semantics beyond what the schema provides (e.g., it doesn't explain format or constraints). With high schema coverage, the baseline score is 3, as the description doesn't compensate but doesn't need to.

  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 with specific verbs ('polish and refine') and the resource ('a prompt'), and it specifies the improvement goals ('better clarity, readability, and flow'). However, it does not explicitly distinguish this tool from its siblings (boost_prompt, crafting_prompt), which would be needed for a score of 5.

  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 its siblings (boost_prompt, crafting_prompt) or any alternatives. It lacks explicit instructions on context, prerequisites, or exclusions, offering only a general purpose without usage differentiation.

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

• crafting_promptB
  3.4/5.0
  Behavior3/5

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

  Annotations indicate read-only and open-world behavior, which the description doesn't contradict, but it adds no behavioral context beyond that—no details on rate limits, authentication needs, or output characteristics. With annotations covering safety, a 3 reflects minimal added value.

  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 without unnecessary details, though it could be slightly more structured by explicitly listing key parameters.

  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 with 6 parameters, low schema coverage, no output schema, and annotations that only cover safety, the description is incomplete—it lacks details on parameter meanings, output format, and usage scenarios, making it inadequate for full understanding.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is low at 33%, with only 'request' and 'constraints' described in the schema. The description mentions 'a few settings' but doesn't explain parameters like 'mode,' 'approach,' 'tone,' or 'verbosity,' failing to compensate for the coverage gap.

  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's purpose with a specific verb ('Generate') and resource ('structured, reusable workflow prompt for complex tasks'), distinguishing it from sibling tools like 'boost_prompt' and 'fix_prompt' by focusing on creating prompts from raw requests rather than enhancing or repairing existing ones.

  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 usage by mentioning 'based on a raw request and a few settings,' suggesting it's for turning user input into prompts, but it lacks explicit guidance on when to use this tool versus alternatives like 'boost_prompt' or 'fix_prompt,' or any context-specific exclusions.

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