Server Quality Checklist

Tool Scores

• evaluate_scoreB
  3.2/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. However, it only restates that a score yields a grade evaluation, omitting details such as the grading scale (e.g., letter grades, pass/fail), output format, or error handling for out-of-range inputs. It adds little beyond the tool's name.

  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?

  The description is a single, compact sentence that immediately conveys the tool's operation. It contains no filler or redundant phrases, making it highly concise and front-loaded with the essential 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?

  For a simple tool with one parameter and a provided output schema, the description is minimally adequate. It states the core purpose clearly but omits specific details about the returned grade evaluation (e.g., possible values or criteria). The existence of an output schema lessens the need to explain return values, so this is a borderline acceptable level of completeness.

  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 schema has zero description coverage for the 'score' property. The description repeats the 0-100 integer range already present in the schema but does not elaborate on what the score represents or how it maps to evaluations. Since there is only one parameter and the description adds no new semantic context, it fails to compensate for the schema's lack of description.

  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 that the tool returns a grade evaluation based on an integer score from 0 to 100. It uses a specific verb (returns) and resource (grade evaluation), and this purpose distinguishes it from sibling tools like calculate_average and get_student_name.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides no guidance on when to use this tool versus calculate_average or get_student_name. It simply states the tool's function without mentioning alternatives, exclusions, or contextual triggers, leaving the agent to infer usage from the tool name alone.

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

• get_student_nameB
  3.2/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It does not explicitly state that this is a read-only operation, nor does it mention possible errors (e.g., student not found) or any restrictions implied by '固定学号'. The lack of detail leaves behavioral traits opaque.

  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?

  The description is a single concise sentence that immediately conveys the purpose. There is no redundancy or extraneous information, and the key information is front-loaded.

  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?

  For a simple lookup tool with an output schema, the description provides the basic purpose. However, the ambiguous phrase '固定学号' could confuse the agent about whether the ID is hardcoded or a parameter. Missing usage context and error behavior make it only minimally viable for the tool's simplicity.

  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 coverage is 0%, so the description must compensate by explaining the parameter. However, it only mentions '学号' (student ID) in passing without clarifying format, range, or acceptable values. This adds minimal meaning beyond the parameter name 'studentId' already visible in 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 action (查询/query) and resource (用户姓名/user name), with the qualifier 根据固定学号 (based on fixed student ID). This distinguishes it from sibling tools like calculate_average and evaluate_score, which are calculation-oriented.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines2/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  No guidance is given about when to use this tool versus the siblings. There is no mention of exclusions, prerequisites, or alternative tools. The usage context is only implied by the tool's obvious purpose, but no explicit guidance is provided.

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

• calculate_averageA
  4.1/5.0
  Behavior4/5

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

  Given there are no annotations, the description carries the burden of transparency. It declares the core behavior (computing an average) and, being a pure calculation with no side effects, nothing more is required. The existence of an output schema covers return format, so the description is sufficiently transparent.

  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?

  The description is a single, concise sentence that immediately states the tool's purpose. It contains no filler words or redundant details, making it optimally front-loaded and easy to scan.

  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, deterministic calculation, the description, combined with the input schema (which defines the three number parameters) and the presence of an output schema, is fully complete. There are no hidden prerequisites, side effects, or complex behaviors that require additional explanation.

  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 input schema has no descriptions for a, b, c, and the description only states that they are 'three numerical values.' This adds little beyond the 'number' type already present in the schema, but since the average is order-invariant, not distinguishing parameters is acceptable. The description provides minimal compensation for the 0% schema coverage.

  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 uses a specific verb ('计算' / calculate) and identifies the exact resource ('三个数值的平均值' / average of three values), clearly distinguishing it from sibling tools like evaluate_score and get_student_name. It precisely states the tool's function without ambiguity.

  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 (when you need to average three numbers) but provides no explicit guidance on when to prefer this tool over alternatives, nor any exclusions. The intended use is obvious from the purpose, but this dimension requires more explicit context for a higher score.

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