Server Quality Checklist

Tool Scores

• create_quizA
  4.4/5.0
  Behavior5/5

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

  Given no annotations, the description fully covers behavioral traits: side effects (read-only, no modifications), authentication (no auth basic, API key for pro), rate limits (10/day free), error handling (structured errors), idempotency, and data privacy. This exceeds 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 clear sections (Args, Behavior, When to use/not use, Behavioral Transparency). While slightly verbose, every section adds value, especially given the absence of annotations.

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

  Completeness3/5

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

  Covers most aspects but lacks description of the return value format. For a quiz creation tool, specifying the output structure (e.g., JSON quiz object) would enhance completeness. No output schema is provided, so this omission is notable.

  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 'Args' section adds meaning beyond the input schema by listing allowed values for difficulty and question_types, default for num_questions, etc. However, the api_key parameter is mentioned in the behavioral section but not in the Args list, slightly reducing clarity.

  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 'Create a quiz with various question types aligned to Bloom's taxonomy,' which is a specific verb+resource. It distinguishes from sibling tools like analyze_student_progress or generate_lesson_plan, which have different purposes.

  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 guidance, though the 'When to use' text ('structured analysis or classification...') is somewhat generic and could be more directly tied to quiz creation. Still, it offers clear context and exclusions.

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

• generate_lesson_planA
  4.4/5.0
  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: it generates structured output without modifying external systems, is deterministic, has no side effects, specifies rate limits (free 10/day, pro unlimited), and states that no authentication is required for basic usage. This is comprehensive.

  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 an intro, Args list, Behavior section, and usage guidelines. It is not overly verbose, though the 'When to use' sections could be slightly more concise. Overall, it is clear and organized.

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

  Completeness3/5

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

  Given 7 parameters, no output schema, and no annotations, the description covers purpose, usage, and most parameters but misses the 'api_key' parameter and does not describe the output format. The 'When NOT to use' is generic. It is adequate but has 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?

  Input schema has 0% description coverage, so the description must compensate. It adds examples for subject, topic, age_group, and duration_minutes, clarifies learning_objectives as optional and auto-generated, and explains differentiation. However, it omits the 'api_key' parameter entirely, leaving it undocumented. This is good but not perfect.

  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 structured lesson plan with objectives, activities, and assessment. The verb 'Generate' and resource 'lesson plan' are specific, and it distinguishes itself from sibling tools like analyze_student_progress, create_quiz, generate_rubric, and recommend_learning_path.

  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, the 'when to use' is somewhat generic and does not directly contrast with siblings, though it mentions rate limits and authentication requirements. This is still above average.

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

• recommend_learning_pathA
  4.4/5.0
  Behavior5/5

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

  Even without annotations, the description thoroughly discloses behavior: read-only, stateless, idempotent, no side effects, authentication requirements, rate limits (free/pro tiers), error handling, and data privacy. This 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.

  Conciseness3/5

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

  The description is well-structured with sections, but contains redundancy: 'Behavioral Transparency' details appear twice (once in the main description and again after 'When NOT to use'). This could be condensed without losing information.

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

  Completeness3/5

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

  The description covers many aspects (behavior, rate limits, error handling) but does not describe the format or structure of the successful output (e.g., what a learning path recommendation looks like). Given no output schema, this gap reduces completeness.

  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 includes an 'Args' section that describes each parameter, adding meaning beyond the input schema's type/title. For example, 'current_level: Current proficiency: beginner, intermediate, advanced' provides values. With 0% schema description coverage, this fully compensates.

  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: 'Recommend a personalized learning path based on student profile.' This is specific and distinct from sibling tools (e.g., analyze_student_progress, create_quiz), which focus on different tasks.

  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, advising to use for structured analysis and cautioning against real-time production use without human review. However, it does not explicitly contrast with sibling tools, but still provides clear guidance.

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

• analyze_student_progressA
  4.5/5.0
  Behavior5/5

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

  With no annotations, the description fully covers behavioral traits: read-only, idempotent, no side effects, authentication requirements, rate limits (10/day free), error handling (structured errors), data privacy (no storage). Comprehensive and clear.

  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-structured with clear sections, though there is minor redundancy between 'Behavior' and 'Behavioral Transparency'. Overall front-loaded and efficient for its detail level.

  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 4 parameters, no annotations, and no output schema, the description is quite complete. It covers purpose, usage, parameters, and behaviors. The only gap is the lack of a success output format description, but structured output is implied.

  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 'Args' section adds meaning to student_name, assessments (list of dicts with specific keys), and target_grade, despite 0% schema coverage. The api_key parameter is also mentioned in behavioral transparency, providing context 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 student performance trends and generate progress report', specifying a concrete verb and resource. It distinguishes from sibling tools like create_quiz and generate_lesson_plan by focusing on analysis and reporting.

  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 'When to use' and 'When NOT to use' sections, advising against real-time decision-making without human review. However, it does not name specific alternative tools for exclusion.

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

• generate_rubricA
  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 carries full burden. It extensively covers side effects (read-only, no state modification), authentication, rate limits, error handling, idempotency, and data privacy, leaving no ambiguity about safety or 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-organized with clear sections (Args, Behavior, When to use/not, Behavioral Transparency). However, it is somewhat verbose, with redundant statements (e.g., 'Behavior' and 'Behavioral Transparency' both mention no side effects). Still, it remains readable and front-loaded.

  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 and 0% schema coverage, the description covers most aspects thoroughly—purpose, usage, behavior, and constraints. However, it omits the output structure (e.g., format of the rubric) and fails to describe the api_key parameter. Overall, quite complete but with minor gaps.

  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%, but the description lists parameters with brief explanations (e.g., 'criteria: Custom assessment criteria (auto-generated if omitted)'). However, the 'api_key' parameter in the schema is not mentioned in the description, and details like enum values for 'assignment_type' are only hinted via examples. Thus, partial compensation.

  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 begins with 'Generate an assessment rubric with detailed criteria and descriptors,' which clearly states the tool's specific verb+resource. It distinguishes itself from siblings like 'analyze_student_progress' and 'create_quiz' by focusing on rubric generation.

  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 dedicated 'When to use' and 'When NOT to use' sections, providing explicit guidance on appropriate contexts (structured analysis/classification) and exclusions (not for real-time production decision-making without human review). It also mentions rate limits for free vs pro tiers.

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