Server Quality Checklist

Tool Scores

• analyze_token_savingsC
  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. It states the tool analyzes 'potential token savings,' implying a read-only, non-destructive operation, but doesn't clarify if it requires specific permissions, how it calculates savings, or what the output format is. 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, clear sentence: 'Analyze potential token savings when converting JSON to TOON.' It is front-loaded with the core purpose, has zero wasted words, and is 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 the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'token savings' means, how the analysis is performed, or what the result looks like (e.g., a percentage, comparison). For a tool with no structured output and minimal behavioral context, more detail is needed to be fully helpful.

  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 one parameter 'json_data' documented as 'JSON data to analyze for token savings.' The description adds no additional meaning beyond this, such as format examples or constraints. With high schema coverage, the baseline score is 3, as the schema adequately handles parameter semantics.

  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: 'Analyze potential token savings when converting JSON to TOON.' It specifies the action (analyze), the resource (token savings), and the context (conversion from JSON to TOON). However, it doesn't explicitly distinguish this analysis tool from its sibling conversion tools (convert_json_to_toon, convert_toon_to_json), which would require a 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 alternatives. It doesn't mention the sibling tools (convert_json_to_toon, convert_toon_to_json) or explain that this is for analysis before conversion, nor does it specify prerequisites or exclusions. The context is implied but not explicit.

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

• convert_json_to_toonB
  3.2/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden but only states the conversion purpose without behavioral details. It doesn't disclose potential side effects, error conditions, performance characteristics, or what the output looks like (though no output schema exists). For a transformation tool with zero annotation coverage, this is inadequate.

  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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality.

  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 transformation tool with no annotations and no output schema, the description is insufficient. It doesn't explain what TOON format is, what the output looks like, or provide any behavioral context. The 100% schema coverage helps with parameters, but overall context for proper tool selection and usage is lacking.

  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%, providing complete parameter documentation. The description doesn't add any parameter-specific information beyond what's in the schema, so it meets the baseline of 3. It mentions 'JSON data' generally but doesn't elaborate on format requirements or constraints.

  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 ('Convert') and resource ('JSON data to TOON format'), specifying the purpose as transformation for efficiency in LLM token usage. It distinguishes from sibling 'convert_toon_to_json' by direction, but doesn't explicitly contrast with 'analyze_token_savings' beyond implied functional difference.

  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 context ('for efficient LLM token usage') but doesn't provide explicit guidance on when to choose this tool over alternatives like 'analyze_token_savings' or when not to use it. The purpose suggests it's for conversion to TOON format specifically, but lacks comparative context.

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

• convert_toon_to_jsonB
  3.2/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden but only states the conversion action without disclosing behavioral traits like error handling, performance characteristics, or what happens with malformed input. It doesn't add meaningful context beyond the basic operation.

  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—every word contributes directly to explaining the tool's function. It's appropriately sized and front-loaded with the core purpose.

  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 and no output schema, the description is incomplete for a conversion tool. It doesn't explain what the JSON output looks like, potential errors, or any side effects, leaving significant gaps in understanding the tool's behavior and results.

  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 the single parameter 'toon_data'. The description adds no additional meaning about parameter usage, format expectations, or examples beyond what the schema provides, meeting the baseline for high 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 verb ('convert') and resource ('TOON format to JSON data'), making the purpose understandable. It distinguishes from sibling 'convert_json_to_toon' by specifying the opposite direction, though it doesn't explicitly name the sibling for comparison.

  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 when TOON data needs conversion to JSON, but provides no explicit guidance on when to use this versus alternatives like 'analyze_token_savings' or prerequisites. The context is clear but lacks specific when/when-not instructions.

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