Server Quality Checklist

Tool Scores

• required_fieldsA
  4.3/5.0
  Behavior5/5

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

  With no annotations provided, the description fully discloses behavioral traits: read-only/stateless, rate limits (free 10/day, Pro unlimited), no authentication required, idempotency, error handling returning structured errors, and data privacy (no storage/transmission). This is exceptionally thorough.

  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 with clear sections (behavior, when to use, args, behavioral transparency). However, there is redundancy: the 'Behavior' section repeats the same read-only/stateless/rate limit info later in 'Behavioral Transparency.' Still, it remains organized and mostly front-loaded with the purpose.

  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 listing tool, the description covers all necessary context: purpose, behavior, rate limits, authentication, error handling, idempotency, and data privacy. An output schema exists (not shown), but the description provides enough cues for safe invocation without needing it.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema has 0% description coverage (no parameter descriptions), so the description must compensate. It only states 'api_key (str): The api key to analyze or process,' which adds minimal meaning—it does not clarify its optional role given 'no authentication required for basic usage.' The compensation is inadequate.

  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 opens with a specific verb-resource pair ('List the 10 required AI-BOM field categories and their fields'), clearly stating what the tool does. This distinct purpose differentiates it from siblings like 'audit_ai_bom_completeness' and 'generate_ai_bom' without needing explicit 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?

  The description includes dedicated 'When to use' and 'When NOT to use' sections, providing clear context for appropriate usage. It advises against real-time production use without human review, but does not explicitly name alternative sibling tools, so it lacks direct differentiation.

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

• audit_ai_bom_completenessA
  4.4/5.0
  Behavior5/5

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

  With no annotations, the description carries full weight and excels: it covers side effects (read-only, no modifications), authentication (none for basic), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. This is comprehensive and exceeds typical annotation coverage.

  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 with clear sections and front-loaded key information. However, there is redundancy between the 'Behavior' and 'Behavioral Transparency' sections, making it slightly longer than necessary. Overall, it is organized and each part serves a purpose.

  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 complexity (2 params, output schema present), the description covers all essential aspects: input, output (pass/fail + gap list), behavioral traits, rate limits, and limitations. The output schema exists, so not detailing return format is acceptable. It is fully 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.

  Parameters3/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The schema has 0% description coverage, so the description must compensate. The 'Args' section provides brief descriptions ('The ai bom json to analyze or process') which adds minimal value beyond the title. The overall context helps, but parameter-specific detail is lacking.

  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 audits an AI-BOM for completeness against 10 required field categories and returns pass/fail and gap list. This specific verb+resource+scope distinguishes it from siblings like generate_ai_bom (creation) and map_to_regulation (mapping).

  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 explicit 'When to use' and 'When NOT to use' sections, offering context for appropriate usage and cautioning against real-time decisions without human review. However, it does not directly compare to sibling tools, missing a chance to differentiate further.

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

• generate_ai_bomA
  4.4/5.0
  Behavior5/5

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

  With no annotations, the description fully covers behavior: read-only, deterministic, no side effects, authentication, rate limits, error handling, idempotency, and data privacy.

  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?

  Well-organized with clear sections and front-loaded purpose, though the 'Args' block is somewhat redundant given schema titles.

  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 complexity (9 params, no schema descriptions) and presence of output schema, the description covers usage, behavior, and output format adequately, but parameter semantics remain weak.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters2/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  Schema description coverage is 0%, but the 'Args' section only repeats parameter names with generic 'to analyze or process' phrasing, adding minimal meaning. Some descriptions may be misleading (e.g., api_key).

  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 generates an AI-BOM in CycloneDX ML-BOM or SPDX 3.0 format for compliance, distinguishing it from siblings like audit_ai_bom_completeness.

  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 'When to use' and 'When NOT to use' sections provide clear guidance, including exclusion for real-time decisions without human review.

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

• map_to_regulationA
  4.7/5.0
  Behavior5/5

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

  With no annotations provided, the description fully details behavior: read-only, stateless, idempotent, safe to repeat, rate limits (10/day free), authentication needs, error handling (structured errors), and data privacy (no storage/logging). This far exceeds typical transparency.

  Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

  Conciseness3/5

  Is the description appropriately sized, front-loaded, and free of redundancy?

  The description is well-structured with headings but is verbose, especially the behavioral transparency section which repeats information from the earlier behavior list. While comprehensive, it could be more concise.

  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, the description covers all necessary aspects: purpose, parameters, behavior, rate limits, error handling, idempotency, and data privacy. Nothing critical is missing for an AI agent to use this 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?

  Despite 0% schema description coverage, the description explains each parameter in an 'Args' section: ai_bom_json, regulation (with default eu_ai_act), and api_key. While explanations are somewhat generic, they add meaningful context beyond the schema's bare definitions.

  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: mapping an AI-BOM against a specific regulatory framework's technical documentation requirements. It lists supported frameworks (eu_ai_act, nist_ai_rmf, us_eo_14028, iso_42001) and is distinct from siblings like audit_ai_bom_completeness or generate_ai_bom.

  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 includes explicit 'When to use' and 'When NOT to use' sections, guiding the AI agent on appropriate contexts. It advises against real-time production use without human review, which sets clear expectations.

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