Server Quality Checklist

Tool Scores

• humanizer_analyze_ai_tellsA
  4.1/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, idempotentHint, and no destruction. The description adds details about the analysis process, risk score range (0-100), and output contents (patterns, recommendations), which goes beyond annotations.

  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 front-loaded with the main purpose, lists patterns, and explains output briefly. It is concise without unnecessary detail, though slightly verbose with 'Args:' and 'Returns:' sections.

  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?

  Despite the lack of output schema, the description explains that the return is a JSON report with risk score, detected patterns, and recommendations. This is sufficient for a simple input tool with one parameter.

  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 provides a description for the 'text' parameter. The description's 'Args' section only reiterates that 'params' contains the text, adding no new meaning beyond the schema. Baseline 3 is appropriate given 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: 'Analyze text for AI-generated patterns and compute a detection risk score.' It lists specific patterns scanned and returns a score, which distinguishes it from siblings like humanizer_humanize_text or humanizer_compare_before_after.

  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 implies usage for detecting AI-generated text, but does not explicitly state when to use this over sibling tools. However, the context of analysis vs. rewriting is clear enough for effective selection.

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

• humanizer_quick_vocab_scanA
  4.3/5.0
  Behavior4/5

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

  Annotations already indicate read-only, non-destructive, and idempotent behavior. The description adds valuable context by stating 'no structural analysis' and detailing the output format ('JSON with found AI words, their positions, and replacement suggestions'). This complements the annotations without contradiction.

  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 and well-structured: a clear one-line purpose, a usage paragraph, and structured Args/Returns sections. Every sentence adds value with no wasted words.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's simplicity, the existence of an output schema, and annotations, the description sufficiently explains what the tool does, when to use it, and what it returns. It distinguishes from sibling tools and covers all necessary aspects.

  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 the note 'Contains the text to scan' for the 'params' argument, but the input schema already defines 'text' with a description. With schema description coverage at 0%, this minimal addition provides some clarification, but not enough to fully compensate.

  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 it's a 'fast scan for AI-associated vocabulary only — no structural analysis.' This specifies the verb (scan), resource (AI-associated vocabulary), and scope (quick, no structural analysis), distinguishing it from sibling tools like humanizer_analyze_ai_tells.

  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 explicitly advises using this tool 'for a quick check when you just want to find and replace AI words without running the full analysis pipeline.' It provides clear context for when to use it and implies alternative usage (full analysis), though it does not explicitly name the alternative tool.

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

• humanizer_compare_before_afterA
  4.4/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, idempotentHint, etc. The description adds detail on what metrics are shown (burstiness, vocabulary tells, structure, risk scores), which goes beyond annotations.

  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?

  Very concise with purposeful sentences. Front-loaded with purpose, followed by usage guidance, then structured parameter and return descriptions. No wasted words.

  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?

  With annotations and output schema present, the description covers purpose, usage, parameters, and return type. Could mention that the rewritten text should be from humanizer_humanize_text, but it's implied by context.

  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?

  Input schema has 0% description coverage, but the 'Args:' section in the description provides clear, human-readable explanations for both parameters, compensating well for the missing 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 tool compares detection metrics between original and rewritten text. It distinguishes itself from siblings like humanizer_humanize_text and humanizer_analyze_ai_tells by focusing on side-by-side comparison.

  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?

  Explicitly says 'Use after humanizing to verify improvement', providing clear when-to-use context. Does not specify when not to use or alternatives, but sibling tools provide implicit differentiation.

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

• humanizer_humanize_textA
  4.4/5.0
  Behavior5/5

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

  Discloses deterministic mechanical pass, lists exact transformations (vocabulary swaps, AI-phrase removal, contraction injection, em-dash cleanup), and notes the returned polish_instructions. Aligns with annotations (readOnlyHint, idempotentHint) without contradiction.

  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?

  Front-loaded with purpose and process; includes necessary post-call instructions. Slightly verbose due to instructions, but every sentence adds value. Could be trimmed slightly.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the tool's single parameter and presence of output schema, the description covers the two-step workflow (tool call then agent refinement) and explains the return fields. Sufficient for correct usage.

  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 has good per-property descriptions; description merely summarizes 'text, text type, and voice preferences'. Does not add significant meaning beyond the schema, but coverage is high, so baseline 3 is appropriate.

  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?

  Description states 'Rewrite AI-generated text to sound human', using specific verb-resource pairing. Differentiates from siblings like humanizer_analyze_ai_tells by focusing on rewriting rather than analysis.

  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?

  Provides explicit post-call instructions for the agent (refine the text). Does not explicitly mention when to avoid this tool or compare to alternatives, but the context is clear enough.

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

• humanizer_get_banned_wordsA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint and idempotentHint, so safety is clear. The description adds that it returns a JSON with two lists (vocabulary and phrase ban lists), which is useful behavioral context beyond annotations.

  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 very concise at four sentences, each serving a purpose: stating the function, suggesting usage, detailing content, and specifying return type. No wasted words.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  For a simple read-only tool with no parameters and rich annotations, the description fully covers purpose, usage context, and output format. The existing output schema provides additional detail, making the description 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?

  No parameters exist, and schema coverage is 100%. The description adds no parameter information, but none is needed. Baseline score of 4 is appropriate.

  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 returns the complete list of AI-associated words and their human replacements, distinguishing it from sibling tools that analyze, compare, or rewrite text.

  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 advises using the list as a reference when manually editing text, providing clear context. However, it does not explicitly exclude alternatives or specify when not to use this tool.

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

• humanizer_get_rewrite_instructionsA
  4.5/5.0
  Behavior5/5

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

  Annotations (readOnlyHint=true, destructiveHint=false, idempotentHint=true) already indicate non-destructive behavior. The description adds that it returns instructions, not rewritten text, and that output is a JSON string with analysis and instructions, aligning with and complementing the annotations.

  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, front-loaded with purpose, and structured with Args/Returns sections. Every sentence adds value without redundancy.

  Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

  Completeness5/5

  Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

  Given the presence of an output schema (though not shown), the description's explanation of return value as 'str: JSON with analysis results and step-by-step rewrite instructions' is sufficient. The tool is well-contextualized among siblings and parameters are defined in 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 0% as the description does not detail parameters, but the schema itself is rich with descriptions for each property (text, text_type, preserve_meaning, voice_notes). The description's mention of 'text, text type, and voice preferences' is a high-level summary, adding little beyond the schema.

  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 'Analyze AI text and return detailed, step-by-step rewrite instructions' and explicitly distinguishes itself by noting 'This tool does NOT rewrite the text itself — it provides a structured action plan.' This differentiates it from sibling tools like humanizer_humanize_text.

  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 implicitly guides usage by stating it provides instructions rather than rewriting, and mentions tailoring to text type and patterns. However, it does not explicitly list when to use this tool vs alternatives, though the sibling context helps.

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