Server Quality Checklist

Tool Scores

• generate_flashcardsA
  4.1/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it generates flashcards with question/answer pairs, uses a content engine, optimizes for spaced repetition, includes error handling with error messages, and raises ValueError for invalid num_cards. It doesn't mention rate limits, authentication needs, or destructive effects, but covers core operational behavior adequately.

  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 appropriately sized and front-loaded with purpose, followed by detailed parameter and return explanations. Every sentence adds value, though the example is lengthy but informative. It could be slightly more concise by integrating some details more tightly, but overall structure is logical and efficient.

  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 annotations, but with output schema), the description is highly complete. It covers purpose, usage context, detailed parameter info, return values (including success/error cases), and provides an example. The output schema exists, so return value explanation in the description is beneficial but not strictly necessary, making this thorough.

  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?

  Schema description coverage is 0%, so the description must compensate fully. It excels by providing detailed parameter semantics: topic as 'subject area, concept, or learning domain' with examples, num_cards with default, range, and optional status, and difficulty with options, default behavior, and optional status. This adds substantial meaning beyond the bare schema.

  Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

  Purpose4/5

  Does the description clearly state what the tool does and how it differs from similar tools?

  The description clearly states the tool's purpose: 'Generate educational flashcards for effective study and memorization' and specifies it uses 'EduChain's content engine' with 'key concepts, definitions, and important facts.' It distinguishes from sibling tools (generate_mcqs, lesson_plan) by focusing on flashcard generation rather than multiple-choice questions or lesson plans. However, it doesn't explicitly contrast with siblings in the description text.

  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 usage for study and memorization with spaced repetition learning, but doesn't explicitly state when to use this tool versus generate_mcqs or lesson_plan. It provides context about educational content generation but lacks specific guidance on alternative selection or exclusion criteria.

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

• generate_mcqsA
  4.3/5.0
  Behavior4/5

  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 effectively describes key behaviors: it uses 'EduChain's QnA engine', generates 'well-structured' questions with 'four options and one correct answer', includes error handling with error messages, and specifies the ValueError exception for parameter validation. However, it doesn't mention rate limits, authentication requirements, or potential side effects.

  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 (purpose, parameters, returns, raises, example) and front-loaded with the core functionality. While comprehensive, some sentences could be more concise (e.g., the returns section is somewhat verbose). Overall, most content earns its place by adding value.

  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, no annotations, and the presence of an output schema, the description is complete enough. It thoroughly explains parameters, return structure (including success/error cases), exceptions, and provides a concrete example. The output schema means the description doesn't need to detail return values beyond what's 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?

  The description adds substantial meaning beyond the input schema, which has 0% description coverage. It provides detailed explanations for both parameters: 'topic' includes purpose, specificity guidance, and concrete examples; 'num_questions' explains default value, valid range, and constraints. This fully compensates for the schema's lack of 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's purpose with specific verb ('generate') and resource ('multiple-choice questions'), and distinguishes it from sibling tools by specifying the type of educational content (MCQs vs flashcards or lesson plans). The opening sentence directly answers what the tool does.

  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 usage context through the 'educational topic' specification and parameter examples, but doesn't explicitly state when to use this tool versus the sibling tools (generate_flashcards, lesson_plan). No guidance is provided about alternative scenarios or exclusions.

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

• lesson_planA
  4.3/5.0
  Behavior4/5

  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 effectively describes the tool's behavior: it uses 'EduChain's content engine,' follows 'educational best practices,' and can be 'customized for different grade levels and durations.' It also details the return structure and error handling. However, it does not mention potential limitations like rate limits, authentication needs, or content generation constraints.

  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 and appropriately sized, starting with a clear purpose statement, followed by functional details, parameter explanations, return values, and an example. Most sentences earn their place by adding value, though some parts could be slightly more concise (e.g., the example is detailed but necessary). It is front-loaded with key information.

  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 (generating educational content), no annotations, and an output schema provided, the description is complete. It explains the tool's purpose, behavior, parameters, return structure (including success and error cases), and includes a practical example. The output schema existence means the description doesn't need to detail return values further, and it adequately covers all necessary contextual aspects.

  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?

  The schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for all three parameters: 'topic' with examples and specificity guidance, 'duration' with examples and default behavior, and 'grade_level' with examples and default behavior. This adds significant meaning beyond the basic schema, ensuring clarity on usage and expectations.

  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: 'Generate a comprehensive, structured lesson plan for a given educational topic.' It specifies the verb ('generate'), resource ('lesson plan'), and scope ('comprehensive, structured'), distinguishing it from sibling tools like 'generate_flashcards' and 'generate_mcqs' which focus on different educational resources.

  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 usage for creating lesson plans with educational best practices and customization for grade levels/durations, but does not explicitly state when to use this tool versus alternatives like the sibling tools. It mentions customization features but lacks explicit guidance on scenarios where this tool is preferred over others or any exclusions.

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