Server Quality Checklist

Tool Scores

• generate_reportA
  3.6/5.0
  Behavior3/5

  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 discloses the output format (base64-encoded PDF) and supported content types, but omits details like filesystem dependencies for image/logo paths, failure behavior on invalid specs, or potential side effects. This is partial transparency — core behavior is covered, but important operational traits are missing.

  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?

  Two sentences that are fully front-loaded with the primary verb and resource. Every clause earns its place — the input format, content summary, and output encoding are all covered with no filler or redundancy.

  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?

  This is a complex tool with a deeply nested, undocumented schema, no output schema, and no annotations. The two-sentence description covers the return value but not the substantial context an agent needs to build a valid `spec`: required keys, `after_section` semantics, theme expectations, default behaviors (page_size, orientation), or filesystem prerequisites for images/logo. The description is far from complete relative to the tool's complexity.

  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 0%, so the description must compensate for the single complex `spec` parameter. It adds value by naming the top-level content types (metadata, sections, tables, charts, images), which helps an agent envision the spec's shape. However, it doesn't explain required sub-structure (e.g., metadata requires a title, sections require heading/body, theme uses RGB color arrays), so the compensation is incomplete.

  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 uses a specific verb ('Generate') and resource ('professional PDF report'), and specifies the exact input format ('structured JSON specification') with supported content types (metadata, sections, tables, charts, images). The phrase 'structured JSON specification' differentiates it from the sibling generate_report_from_text, which presumably handles unstructured text input.

  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 phrase 'from a structured JSON specification' implies the tool is the right choice when structured JSON input is available, but it never explicitly states when to use it versus generate_report_from_text, nor does it mention exclusions or preconditions. The usage context is implied rather than explicitly stated.

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

• generate_report_from_textA
  3.9/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It discloses the automatic section detection behavior and the output being a structured PDF. However, it does not explain how the PDF is returned (e.g., file path or download link), any side effects like file creation, or limitations on input size/format, leaving gaps in behavioral transparency.

  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 concise, two sentences, and front-loaded with the core action. Every phrase adds value, and it avoids redundancy with the schema.

  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 no output schema and no annotations, the description is only moderately complete. It covers the main function and input, but omits details about the return value, how the tool differs from sibling 'generate_report', and any edge cases or preconditions. For a 6-parameter tool, more context would be needed for full autonomous invocation.

  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 baseline is 3. The description adds the concept of 'basic metadata' (mapping to author, company, theme, classification) and notes auto-detection, but does not provide additional parameter-specific semantics beyond what the schema already details.

  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 uses the specific verb 'Convert' and clearly identifies the resource: 'raw text or LLM chat output' transformed into a 'structured PDF report'. This directly distinguishes it from siblings like 'generate_report' by focusing on unstructured text input and automatic section detection.

  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: use when you have unstructured text or LLM output to convert into a PDF report. It does not explicitly name alternatives or exclusions, but the phrase 'Pass unstructured text and basic metadata' gives actionable guidance on when this tool applies.

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

• list_themesA
  4/5.0
  Behavior3/5

  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. The verb 'List' inherently implies a non-mutating read operation, which is a useful cue. However, it does not describe the return format or any potential edge cases (e.g., empty list, sorting). The transparency is adequate for a simple listing tool but not rich.

  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 with no wasted words. It is front-loaded with the action verb and resource, making it easy to scan.

  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 simplicity (no parameters, no output schema, no annotations) and the context from sibling tools, the description provides sufficient information for an agent to understand what the tool does. It could be improved by mentioning the output format, but the current level is adequate for a list operation.

  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 tool has zero parameters, so there are no parameter descriptions to add meaning. Per the baseline for 0-param tools, a score of 4 is appropriate; the description does not need to compensate for missing schema detail since there is none.

  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 uses a specific verb ('List') and a clear resource ('available color themes for PDF reports'). It clearly distinguishes itself from sibling tools by indicating it provides a themed list, while siblings are for generating reports.

  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 this tool is a prerequisite for generating reports with themes, but it does not explicitly state when to use it vs alternatives or mention any exclusions. Since it is the only listing tool among siblings, the intended usage is apparent but not explicitly guided.

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