Server Quality Checklist

Tool Scores

• list_departmentsA
  4.2/5.0
  Behavior3/5

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

  No annotations provided, so description carries burden. Mentions 'snapshot' implying read-only read, but does not explicitly state that it is non-destructive or what the snapshot entails. Could be more transparent about behavior.

  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?

  Description is compact, front-loaded with purpose, then parameter, then return fields. Slightly dense due to code encoding details, but still concise overall.

  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 one parameter and an output schema, description covers the core: purpose, parameter usage, return fields. Could elaborate on 'snapshot' meaning or limitations, but adequate for the tool's simplicity.

  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 coverage is 0%, but description fully explains the query parameter: default empty, filters by official department name substring, and gives examples ('財務', '法律'). Adds meaning 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?

  Clearly states the tool lists department codes (snapshot) and distinguishes from siblings like search_courses and get_syllabus by specifying the resource (department codes) and the operation (list).

  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 guidance on using the query parameter for filtering by official department name substring, with examples. Also hints that the code can be fed to search_courses. Lacks explicit when-not-to-use scenarios.

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

• get_syllabusA
  4.3/5.0
  Behavior3/5

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

  No annotations provided, so description must cover behavioral traits. It mentions plain text output and content categories but does not disclose other aspects like authentication, rate limits, or destructive potential. Adequate but not exhaustive.

  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?

  Three sentences efficiently convey purpose, input source, and content. No redundant information; each sentence earns its place.

  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 tool with one parameter and an output schema present, the description fully addresses what the tool does, how to use it, and what it returns. Complete for the given context.

  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% parameter description coverage, so description adds crucial meaning by specifying that syllabus_url comes from search_courses. This compensates well for the schema's lack of detail.

  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 clearly states the tool reads the full syllabus text of a course, listing typical contents. It distinguishes itself from siblings (list_departments, search_courses) by focusing on retrieval of detailed syllabus data.

  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?

  Explicitly instructs that syllabus_url should come from search_courses, providing clear sourcing direction. Lacks explicit when-not-to-use or alternative tools, but the guidance is strong given the sibling context.

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

• search_coursesA
  4.3/5.0
  Behavior4/5

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

  Discloses that the tool performs a real-time query against a specific API, describes the return structure, and notes that remain_url requires a separate action. No annotations are provided, so the description carries the burden, and it does so adequately without 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?

  Well-structured with a header, parameter list, and return format. Uses bullet points for readability. Slightly verbose but every sentence adds value.

  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 all important aspects: parameters, return structure, and related tool. Lacks edge-case handling (e.g., error scenarios) but is sufficient for effective use.

  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 compensates by detailing each parameter with format and examples (e.g., semester format '1151', dept examples '財務管理學系' or '357', keyword optional). Adds meaning 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 the verb (查/查詢), resource (開課清單/courses), and scope (by department and semester). It explicitly references the API source and distinguishes from sibling tools like list_departments for department code lookup.

  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 guidance on how to use parameters: semester format, dept as name or code, and keyword as optional. Directs the user to list_departments for code lookup. Does not specify when to avoid using this tool, but the context is clear.

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