Server Quality Checklist

Tool Scores

• decode_plantumlC
  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 decodes encoded strings but doesn't cover error handling (e.g., invalid input), performance traits, or output format (e.g., returning plain text PlantUML code). This leaves significant gaps in understanding how the tool behaves beyond its basic function.

  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 with zero waste. It is appropriately sized and front-loaded, making it easy for an agent to parse quickly without unnecessary elaboration.

  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 the decoded output looks like (e.g., PlantUML code format), error conditions, or dependencies on sibling tools. For a tool with no structured data beyond the input schema, more contextual information is needed to guide effective use.

  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 schema description coverage is 100%, with the parameter 'encoded_string' fully documented in the schema. The description adds no additional meaning beyond what the schema provides (e.g., format details or examples), so it meets the baseline score of 3 for high schema coverage without extra value.

  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 ('decode') and resource ('encoded PlantUML string'), and it distinguishes the action from encoding or generating diagrams. However, it doesn't explicitly differentiate from sibling tools like 'encode_plantuml' beyond the opposite direction, leaving some room for improvement.

  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 prerequisites (e.g., needing an encoded string), exclusions, or comparisons to sibling tools like 'encode_plantuml' or 'generate_plantuml_diagram', leaving the agent to infer usage context.

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

• encode_plantumlB
  3.1/5.0
  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 but only states the basic function. It doesn't disclose behavioral traits such as encoding format, error handling, performance, or security aspects (e.g., input validation), leaving significant gaps for a tool that likely transforms data.

  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 any wasted words. It's appropriately sized and front-loaded, making it easy to understand 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 simple input schema (1 parameter, 100% coverage) and no output schema, the description is minimally adequate but incomplete. It lacks details on output format, error cases, or integration context, which are important for a tool that encodes data for URLs.

  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 no parameter-specific information beyond the schema, which has 100% coverage and fully documents the single parameter. This meets the baseline of 3, as the schema handles the heavy lifting without extra value from the description.

  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 action ('Encode') and the resource ('PlantUML code'), specifying it's for URL usage. It distinguishes from siblings like 'decode_plantuml' (reverse operation) and 'generate_plantuml_diagram' (creation), but doesn't explicitly contrast them in the description.

  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 minimal guidance by mentioning 'for URL usage', implying use when embedding PlantUML in URLs. However, it lacks explicit when-to-use scenarios, prerequisites, or alternatives (e.g., when to use vs. generate_plantuml_diagram), offering no detailed context for selection.

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

• generate_plantuml_diagramA
  4.2/5.0
  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 and does well by disclosing key behavioral traits: it performs automatic syntax validation, returns embeddable image URLs or structured error details for auto-fix, and optionally saves to a local file with directory restrictions. However, it lacks details on rate limits, authentication needs, or error handling specifics.

  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 with the core purpose and key features in the first sentence, followed by additional details in a structured manner. Every sentence adds value without redundancy, making it efficient and easy to parse.

  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 (3 parameters, no output schema, no annotations), the description is fairly complete, covering the main functionality, validation, outputs, and optional file saving. However, it could benefit from more details on error handling or response formats to fully compensate for the lack of output schema.

  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 parameters thoroughly. The description adds minimal value beyond the schema, mentioning 'automatic syntax validation' for plantuml_code and 'Optionally saves the diagram to a local file' for output_path, but does not provide additional semantic context. This 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 specific action ('Generate a PlantUML diagram') and distinguishes it from siblings by mentioning 'automatic syntax validation and error reporting for auto-fix workflows' and 'Returns embeddable image URLs for valid diagrams or structured error details for invalid syntax that can be automatically corrected.' This goes beyond just restating the name and differentiates from decode_plantuml and encode_plantuml.

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

  Usage Guidelines4/5

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

  The description provides clear context for when to use this tool ('Generate a PlantUML diagram with automatic syntax validation and error reporting for auto-fix workflows'), but does not explicitly state when not to use it or mention alternatives like the sibling tools. The guidance is implicit rather than explicit about tool selection.

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