Server Quality Checklist

Tool Scores

• get_server_infoB
  3.3/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full burden. It implies a read-only query returning static information, but does not explicitly state side-effect-freeness, idempotency, or authorization needs. The description is adequate but minimal.

  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 captures all essential information without redundancy or waste.

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

  Completeness2/5

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

  The description lists returned categories but lacks details on structure or format. Since there is no output schema, it should describe the response fields. Additionally, no usage context or examples are provided, making it incomplete for an agent to fully understand the tool's output.

  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 input schema has zero parameters, so the baseline score is 4. The description adds value by listing the categories of returned data, which compensates for the lack of parameters.

  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 returns server/engine version, supported unit systems, limits, and tool/error code list. The verb '반환한다' (returns) and the specific resources are clear. However, it does not explicitly distinguish from sibling tools like compute_neutral_axis or run_sensitivity_analysis, which are not about retrieving static info but computation.

  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 on when to use this tool versus alternatives. It simply describes what it returns, without mentioning prerequisites, when it is appropriate, or when to avoid it.

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

• compute_neutral_axisA
  4.1/5.0
  Behavior4/5

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

  With no annotations, the description bears full burden. It discloses output formats (midplane/bottom/ζ), behavior for symmetric laminates, and references axis_definition for assumptions. This is transparent for a compute tool.

  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?

  Description is concise, uses bullet points for clarity, starts with the main purpose, and every sentence adds value. No redundancy.

  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 no output schema and 0% schema coverage, the description explains modes and output but omits details about the laminate parameter structure. User may need external knowledge to provide correct input.

  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 has 0% description coverage. The description compensates by explaining mode options with formulas, but does not elaborate on the laminate object structure, which is required and has additionalProperties. 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 clearly states the tool calculates neutral axis position, specifies two modes with explanations, and describes the output format. It distinguishes itself from sibling tools by focusing on neutral axis computation.

  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?

  No explicit guidance on when to use this tool vs siblings. The context implies it's for structural analysis, but no when-not-to-use or alternative suggestions. Usage is implied by the task domain.

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

• run_sensitivity_analysisA
  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. It discloses the algorithm (central difference), deterministic nature, optimization (reduces calls), and error handling (returns E500 if time budget exceeded). It does not mention side effects (likely none) or data persistence, but covers key operational traits.

  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 exceptionally concise, with three focused sentences. The first sentence states the core function, the second the use case, and the third key behavioral details (error handling, optimization). No wasted words.

  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 the tool's complexity (multiple perturbation parameters, nested laminate input, no output schema), the description omits critical details: the format of the return value (e.g., a table or object) and the structure of the laminate parameter. This forces the agent to guess or risk incorrect invocation.

  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 coverage is 0%, so the description must add meaning. It explains that modulus_rel, thickness_rel, and angle_delta_deg are perturbation sizes for each ply, and that laminate is required. However, it does not describe the laminate object structure or what values the defaults represent, leaving significant gaps.

  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 computes sensitivity of D̂11, coupling_ratio, ζ_x to ply-level perturbations in angle, thickness, and modulus. This is specific and distinguishes it from sibling tools like compute_neutral_axis, which focus on different computations.

  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 explicitly states the use case (manufacturing tolerance robustness assessment) and notes that the tool reduces repeated agent calls into a single call, implying it should be used instead of multiple iterative calls. However, it does not explicitly contrast with sibling tools beyond the implied domain difference.

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

• get_reference_casesA
  4.2/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It correctly implies a read-only operation ('returns'), but does not disclose other behavioral traits like idempotency, rate limits, or safety. The description adds some value by stating the content of each case (payload and expected value), but is not fully 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 two concise sentences. The first states the primary purpose and conditional behavior; the second explains additional value. Every sentence is purposeful, with no redundancy or verbosity.

  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 the tool's simplicity (one optional parameter, no output schema), the description adequately covers behavior (list vs detail), content (payload and expected values), and use cases. It could mention output format or pagination, but not critical. A minor gap in completeness.

  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 description coverage is 0%, so the description must compensate. It adds meaning to the single parameter case_id by stating that omission returns a list, which the schema alone does not convey. This is a clear addition 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 tool 'returns built-in reference cases' and distinguishes it from sibling tools (compute_neutral_axis, run_sensitivity_analysis, get_server_info) which perform different tasks. It specifies the optional case_id parameter for filtering, leaving no ambiguity about the tool's purpose.

  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?

  While it doesn't explicitly compare to siblings, the description indicates usage contexts: as few-shot examples and server self-validation. This gives implicit guidance on when to use the tool. However, it lacks explicit when-not-to-use or alternative recommendations.

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