Server Quality Checklist

Tool Scores

• get_slop_statsA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It discloses that data is 'static local data, no network', indicating fast, non-networked, read-only behavior. It doesn't mention auth or side effects, which are not needed for a static data query.

  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: the first lists what is returned, the second gives usage guidance and a behavioral note. No redundant information; front-loaded with key output details.

  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 zero parameters and no output schema, the description adequately covers the tool's output: it enumerates the main components (distribution stats, tell counts, low-scoring domains, score distribution). The behavioral note adds completeness.

  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 in the schema, so the description naturally does not need to add parameter meaning. However, it effectively adds value by detailing the output content (reference distribution, tell frequencies, etc.), compensating for the lack of an output 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 explicitly states the tool returns reference distribution, tell frequencies, lowest-scoring domains, and score distribution for grade_landing_copy. It clearly distinguishes from sibling tools (check_ai_slop, grade_landing_copy) by focusing on benchmarking statistics.

  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 gives explicit usage context: 'Use it to tell a user whether their score is actually good.' It implies the tool is for interpreting scores from grade_landing_copy, though it doesn't explicitly exclude other uses or mention alternatives.

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

• grade_landing_copyA
  4.6/5.0
  Behavior5/5

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

  With no annotations provided, the description fully carries the burden of behavioral disclosure. It clearly states the tool is 'Deterministic, no LLM, no network' and details the output structure (score, verdict, sub-scores, flags, rewrites). It also notes score comparability to a public dataset. No contradictions.

  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 three sentences long, front-loading the core purpose and dimensions. Every sentence adds unique information: the scoring behavior, the output details, and the comparability context. While slightly dense, it remains efficient and clear.

  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 moderate complexity (3 parameters, no output schema), the description comprehensively covers what the tool does, how it behaves (deterministic), and what it returns. It also connects to sibling tools indirectly via the dataset mention, making it fully informative for an agent to select and invoke the tool correctly.

  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 input schema already covers all 3 parameters with descriptions (100% coverage), so the baseline is 3. The description adds value by explaining how each parameter relates to the scoring dimensions (e.g., 'empty CTA scores 0 on that dimension') and the optional nature of subhead and cta. This goes beyond the schema, earning a 4.

  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: scoring landing-page hero copy across five weighted dimensions. It uses specific verbs ('Score') and resource ('landing-page hero copy'), and the explicit mention of dimensions, return values, and comparability to a public dataset effectively distinguishes it from sibling tools like 'check_ai_slop' and 'get_slop_stats'.

  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 strong context on when to use this tool (scoring landing pages) and highlights key properties like determinism and no network dependency, which imply when not to use it (e.g., when you need generative AI or external data). However, it does not explicitly state alternatives or exclusions, so a 4 is appropriate.

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

• check_ai_slopA
  4.7/5.0
  Behavior5/5

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

  With no annotations, the description fully explains behavior: it is deterministic, local (no LLM call, no network), and always returns the same number for the same input. It also outlines the output structure: score, verdict, per-dimension sub-scores with raw counts, and up to 6 concrete fixes. This exceeds the burden typically required without 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 three sentences, each adding essential information: purpose and measurement components, output details, and deterministic/local nature. No word is wasted, and critical caveats are included up front.

  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 simple input schema and lack of output schema, the description covers all necessary context: what input is expected, how to interpret results, and behavioral guarantees. It also addresses edge cases (short text) and limitations (style vs. authorship), making it sufficient for an agent to use correctly.

  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 sole parameter 'text' is described with a helpful note about reliability at ~200 characters and behavior for shorter text. Since schema coverage is 100%, the description adds value beyond the schema by providing contextual usage hints, earning a 4.

  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: scoring prose on a human-likeness scale from 0 to 100. It specifies exactly which style tells are counted (LLM word/phrase density, em-dash density, formulaic structures, etc.), distinguishing it from siblings like 'grade_landing_copy' which likely targets landing pages.

  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 usage guidance by noting the minimum character count for reliable results and that shorter text still works but skips a dimension. It also warns that this measures style, not authorship, so it's not an AI detector. However, it does not explicitly compare to sibling tools or state when not to use them.

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