Server Quality Checklist

Tool Scores

• generate_model_cardA
  4.5/5.0
  Behavior5/5

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

  With no annotations provided, the description fully compensates by detailing side effects (read-only, no side effects), authentication (basic free, optional API key for pro), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. This thorough coverage exceeds typical expectations.

  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 sections (Args, Behavior, When to use, Behavioral Transparency) and front-loaded with purpose. However, it is verbose, with some repetition between the 'Behavior' section and the bullet points under 'Behavioral Transparency,' slightly reducing conciseness.

  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 no output schema, the description could elaborate on the model card's output structure beyond 'structured transparency information.' Nonetheless, it covers parameters, usage, behavior, and error handling thoroughly, making it mostly complete for an MCP tool with moderate complexity.

  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's 'Args' section adds meaning beyond titles: 'leave empty if not available' for training_data, 'Optional MEOK API key for pro tier' for api_key. However, model_name and purpose descriptions are minimal; more detail (e.g., format constraints) would elevate it further.

  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 'Generate an EU AI Act compliant model card with structured transparency information,' specifying a concrete verb and resource. It differentiates from sibling tools like 'create_impact_assessment' and 'transparency_audit' by emphasizing structured analysis against frameworks.

  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?

  Explicit 'When to use' and 'When NOT to use' sections provide clear context: use for structured analysis, avoid for real-time decisions without human review. However, it does not directly name sibling alternatives for those use cases, missing a small opportunity for even better guidance.

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

• quick_scanA
  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 carries the full burden and excels. It details side effects (read-only, stateless), authentication, rate limits, error handling, idempotency, and data privacy comprehensively.

  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 sections and front-loaded purpose. While thorough, it is somewhat verbose; every sentence earns its place, but brevity could be improved 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 simplicity (one parameter, no output schema), the description is remarkably complete. It covers usage, privacy, rate limits, error handling, and idempotency, leaving no significant gaps.

  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 schema has one parameter 'description' with 0% coverage. The description adds context by stating the input should be a description of an AI system, but lacks additional details like format or examples.

  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: 'Describe an AI system -> instant transparency and explainability assessment.' It uses a specific verb and resource, and distinguishes itself from siblings by emphasizing instant results and no API key required.

  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 explicit 'When to use' and 'When NOT to use' sections, providing clear context. However, it does not explicitly name alternative tools from the sibling list for differentiation.

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

• explain_decisionA
  4.7/5.0
  Behavior5/5

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

  Description fully compensates for missing annotations by detailing behavior: no side effects, deterministic output, rate limits (free/pro tiers), and authentication needs. Contradiction is false.

  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?

  Well-structured: summary line, Args, Behavior, usage sections. Every sentence adds value; no wasted words. Appropriate length.

  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?

  Covers purpose, parameters, behavior, and usage guidelines. Lacks explicit output format description; mentions 'structured output' but could clarify whether it's text or JSON. No output schema provided.

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

  Parameters5/5

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

  With 0% schema coverage, description explains each parameter: 'decision' (the AI decision), 'factors' (comma-separated, optional for generic), and 'api_key' (optional for pro tier). Adds critical meaning beyond schema titles and types.

  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 'Generate a human-readable explanation of an AI decision', which is a specific verb and resource. It clearly distinguishes from siblings like create_impact_assessment or generate_model_card.

  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?

  Includes explicit 'When to use' and 'When NOT to use' sections, providing context for when the tool is appropriate. However, the 'When to use' description is somewhat generic and does not directly compare to sibling tools.

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

• transparency_auditA
  4.7/5.0
  Behavior5/5

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

  Despite no annotations, the description contains a dedicated 'Behavioral Transparency' section that comprehensively covers side effects (none), authentication (none for basic, API key for pro), rate limits (10/day free, unlimited pro), error handling (structured errors), idempotency (fully idempotent), and data privacy (no storage/logging). This fully compensates for missing 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 longer but well-organized with clear headings (Args, Behaviors, When to use, Behavioral Transparency). Every section adds value, though some sentences could be combined without losing clarity.

  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?

  Covers purpose, parameters, behavioral details, usage guidelines, and error handling comprehensively. Lacks explicit mention of return format or output fields, which would be ideal since no output schema exists. Minor gap given the tool's complexity.

  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?

  Schema coverage is 0% (no individual parameter descriptions in schema), but the description explains both parameters: 'system_description' is detailed in the Args block, and 'api_key' is mentioned as optional for pro tier. While not exhaustive, it adds meaningful context beyond raw 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?

  Clearly states the tool assesses an AI system against EU AI Act Article 13 transparency requirements. The verb 'assess' and resource are specific, and it distinct from siblings like 'explain_decision', which targets individual decisions, and 'generate_model_card', which creates a standard documentation format.

  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?

  Provides explicit 'When to use' and 'When NOT to use' sections. It recommends use for structured analysis against frameworks and explicitly warns against real-time production decision-making without human review, offering clear guidance on appropriate contexts.

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

• create_impact_assessmentA
  4.8/5.0
  Behavior5/5

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

  The description explicitly states the tool generates structured output without modifying external systems, has deterministic output for identical inputs, and no side effects. It also notes no authentication required for basic usage. This is comprehensive behavioral disclosure.

  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 bullet points, making it easy to read. While informative, it could be slightly more concise (e.g., merging the behavior section lines), but overall it earns its length.

  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?

  The description covers purpose, usage, parameters, and behavior. It mentions output is a template but does not specify the exact structure or format. Given no output schema, a bit more detail on output would improve completeness, but it is still adequate.

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

  Parameters5/5

  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: system_name as 'Name of the AI system', affected_groups with example and default behavior, api_key as optional for pro tier. This fully compensates for 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 it generates a DPIA/AIIA template for an AI system. It names the specific output type and lists parameters. It distinguishes from sibling tools (explain_decision, generate_model_card, quick_scan, transparency_audit) by focusing on impact assessment.

  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 provides explicit 'When to use' and 'When NOT to use' sections, guiding the agent on appropriate contexts. It also mentions rate limits (free tier: 10/day, pro tier: unlimited) and authentication requirements, giving clear usage boundaries.

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