Server Quality Checklist

Tool Scores

• json_schemaB
  3.4/5.0
  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 generating a schema but lacks details on error handling, output format, performance considerations, or any constraints like rate limits or authentication needs, leaving significant gaps for a tool that processes external resources.

  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 front-loaded and concise, consisting of two clear sentences that directly state the tool's function and parameter requirement without any redundant or unnecessary information, making it highly efficient.

  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 in handling external JSON sources and no output schema or annotations, the description is incomplete. It fails to address critical aspects like the format of the generated TypeScript schema, error responses for invalid inputs, or limitations, which are essential for effective use by 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 description adds minimal semantics beyond the input schema, which already has 100% coverage. It reiterates that the parameter is a 'file path or HTTP/HTTPS URL' but does not provide additional context like supported file formats, URL protocols beyond HTTP/HTTPS, or examples, so it meets the baseline for high schema coverage.

  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 ('TypeScript schema for a JSON file or remote JSON URL'), distinguishing it from sibling tools like json_dry_run and json_filter by focusing on schema generation rather than validation or filtering.

  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 specifying the input type ('JSON file or remote JSON URL'), but does not explicitly state when to use this tool versus alternatives like json_dry_run or json_filter, nor does it provide exclusions or prerequisites for usage.

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

• json_dry_runA
  4.5/5.0
  Behavior4/5

  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 effectively describes key behaviors: it returns size information in bytes, mirrors the shape structure in output, and handles arrays by returning total size of all matching elements. However, it lacks details on error handling, performance implications, or rate limits, which could be relevant for a tool processing potentially large JSON files.

  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 well-structured and front-loaded with the core purpose, followed by detailed parameter explanations. It uses bullet points and examples efficiently, but could be slightly more concise by integrating the 'Note' section into the main text or reducing redundancy in examples.

  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?

  Given the tool's complexity (analyzing JSON size with shape objects) and lack of output schema, the description does a good job of explaining what the tool returns. It covers input semantics and behavioral traits adequately. However, without annotations or output schema, it could benefit from more detail on error cases or output format specifics to be fully complete.

  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?

  The schema description coverage is 100%, so the baseline is 3. The description adds significant value by explaining the 'shape' parameter with detailed examples and notes on how it affects output (e.g., using 'true' for total size, nested objects for breakdowns, and array handling). This clarifies semantics beyond the schema's technical definition, though it doesn't add much for 'filePath' beyond what the schema already states.

  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: 'Analyze the size breakdown of JSON data using a shape object to determine granularity.' It specifies the verb ('analyze'), resource ('JSON data'), and method ('using a shape object'), distinguishing it from sibling tools like json_filter and json_schema by focusing on size analysis rather than filtering or schema extraction.

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

  Usage Guidelines5/5

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

  The description provides explicit guidance on when to use this tool: 'Use json_schema tool to understand the JSON structure first.' This indicates a prerequisite step, helping the agent sequence operations correctly and avoid misuse by analyzing data without prior structural understanding.

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

• json_filterA
  4.5/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and adds valuable behavioral context: it explains automatic chunking when data exceeds 400KB, describes how arrays are handled ('applied to each item'), and mentions the need for valid JSON formatting. It doesn't cover error handling or performance limits, but provides substantial operational guidance.

  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 appropriately sized and front-loaded with the core purpose. The examples section is extensive but necessary for understanding the shape parameter. The workflow guidance about sibling tools is efficiently placed at the end. Some minor redundancy exists between the initial description and shape parameter examples.

  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?

  For a tool with 3 parameters, no annotations, and no output schema, the description provides substantial context: clear purpose, sibling tool relationships, behavioral details (chunking, array handling), and extensive parameter examples. The main gap is lack of information about return format or error conditions, but overall coverage is strong given the complexity.

  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 description coverage is 100%, so baseline would be 3. The description adds meaningful context beyond schema: it provides concrete examples of shape parameter usage (5 detailed examples), explains the 'true' value convention, and clarifies array handling. However, it doesn't add semantic context for filePath or chunkIndex beyond what's in their schema descriptions.

  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 specific action ('filter JSON data'), resource ('JSON data'), and mechanism ('using a shape object to extract only the fields you want'). It distinguishes from siblings by mentioning them as preparatory tools (json_schema, json_dry_run) rather than alternatives for the same filtering purpose.

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

  Usage Guidelines5/5

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

  Explicit guidance is provided on when to use this tool vs alternatives: 'Use json_schema tool to analyse the JSON file schema before using this tool' and 'Use json_dry_run tool to get a size breakdown of your desired json shape before using this tool.' This clearly establishes a recommended workflow with sibling tools.

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