Server Quality Checklist

Tool Scores

• run_architecture_checkC
  2.8/5.0
  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 only says the check is 'fake,' hinting at no real impact, but it does not explain side effects, permissions, rate limits, or expected behavior. The term 'fake' is ambiguous and lacks elaboration.

  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, front-loaded sentence with no wasted words. It is highly concise and structurally appropriate for a simple tool.

  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?

  Given the lack of annotations, the description is incomplete. It provides minimal context about what the check does, what 'fake' means, or what the output represents. The presence of an output schema is not detailed, and the description does not compensate for the missing behavioral context.

  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 description coverage is 0%, and the description does not mention 'service_name' at all. The parameter name is self-explanatory, but the description adds no semantics about format, constraints, or how the service_name is used. It fails to compensate for the lack of schema descriptions.

  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 action ('Run') and the object ('a fake internal architecture check') for a service. It distinguishes itself from sibling tools which are all read-only data retrieval (get_*). However, it lacks specificity about what the check entails, so it stops short of a 5.

  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 alternatives. It does not mention any exclusions or refer to sibling tools. There is no context for appropriate use cases.

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

• get_api_endpoint_infoB
  3.3/5.0
  Behavior2/5

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

  With no annotations, the description carries the burden. 'Retrieve' implies read-only, but it does not disclose authentication requirements, error handling, pagination, or other behavioral traits. The output schema covers return format, but other aspects are absent.

  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?

  Two sentences, front-loaded with the purpose, and no fluff. The example list is directly useful and efficient.

  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 output schema handles return values, but the description lacks usage context, exclusions, and related tool guidance. For a simple single-parameter lookup, this is minimally sufficient but not fully comprehensive.

  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 compensate. It provides example API names ('customer-onboarding', 'product-catalog') which add format context, but does not fully define the parameter's meaning beyond those examples.

  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 ('Retrieve') and resource ('API catalog information for an internal API'), with example API names to clarify scope. It distinguishes itself from sibling tools by targeting API catalog info rather than customer or product data.

  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 explicit guidance on when to use this tool versus alternatives. The examples imply what api_name should be, but there is no stated context or exclusion, leaving the agent to infer usage from sibling names.

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

• get_product_infoA
  4.3/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the disclosure burden. It adds valuable context by stating the data is 'fake internal', which sets expectations about its nature. The verb 'Retrieve' strongly implies a read-only operation, covering the safety profile despite lacking explicit annotations.

  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 sentences, front-loaded with the action and purpose, and every sentence adds value. The examples are concise and directly useful for invoking the tool.

  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 covers what it does, how to pass the product ID, and gives realistic examples. No additional return format explanation is needed since the output schema exists.

  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 coverage is 0%, but the description compensates by giving concrete example values (MORTGAGE-FLEX, PAYMENT-PLUS, BUSINESS-ACCOUNT) and clarifying that the parameter is a product ID. This adds meaning beyond the bare 'string' type 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 uses a specific verb 'Retrieve' with a clear resource 'fake internal product information' and method 'by product ID'. This clearly distinguishes it from sibling tools like get_customer_profile and get_api_endpoint_info.

  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 by providing example product IDs, but it does not explicitly state when to use this tool over alternatives or mention exclusions. Sibling names suggest distinct purposes, but the description itself offers no direct comparison.

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

• get_customer_profileA
  4.4/5.0
  Behavior4/5

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

  With no annotations, the description discloses that the profile is 'fake internal', indicating a mock/test context. The read-only nature is implied by 'Retrieve'. Example IDs provide concrete behavioral context, though error handling is not discussed; the output schema likely covers return shape.

  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 exactly two sentences with no filler. It front-loads the purpose and then provides useful examples, every word earning its place.

  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?

  For a simple 1-parameter lookup tool with an output schema present, the description is nearly complete. It includes 'fake internal' context and example IDs, making it usable. However, it could explicitly state when not to use it (e.g., for real production data) or reference sibling alternatives for contrast.

  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 coverage is 0%, so the description compensates by providing example customer IDs (CUST-1001, CUST-2002), which clarifies the expected format beyond the schema's bare string type. This adds meaningful parameter guidance.

  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 ('Retrieve') and resource ('customer profile'), clearly distinguishing it from siblings like get_product_info and get_api_endpoint_info. The 'fake internal' qualifier adds specificity about the data source.

  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?

  Context is clear: this tool retrieves customer profiles, and example IDs guide usage. It does not explicitly mention alternatives or exclusions, but the sibling tools are in different domains, making implicit differentiation sufficient.

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