Server Quality Checklist

Tool Scores

• get_complaintsA
  3.9/5.0
  Behavior3/5

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

  With no annotations, the description discloses that the tool returns totals per component and truncated narratives, and is non-destructive. However, it doesn't mention rate limits or data freshness, but the behavioral description is adequate for a query 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?

  Four concise sentences, front-loaded with the purpose, followed by usage guidance, output description, and a practical hint. No wasted words.

  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 an output schema exists, the description adequately explains the output structure (grouped by component, totals, truncated narratives). It covers when to use and gives a usage hint. Missing details like error conditions but otherwise complete.

  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 only mentions the limit parameter ('Increase limit for more narratives'). It does not elaborate on make, model, or model_year, leaving them self-explanatory but not adding extra meaning.

  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 retrieves consumer complaints for a vehicle grouped by component, specifying the data source (NHTSA). It distinguishes from sibling tools like recalls, VIN decoding, and safety ratings by focusing on owner-reported problems.

  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 says to call when the user asks about known problems, reliability issues, or defects. Offers a hint to increase limit for more narratives. While it doesn't mention when not to use, 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.

• decode_vinA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It adds some behavioral context by mentioning the output details and the optional model_year optimization, but does not disclose potential limitations, error handling, or data freshness. This leaves room for improvement.

  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 three sentences, each adding value: purpose, usage context, and parameter hint. No redundant information, perfectly concise.

  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 presence of an output schema (implied), the description covers the tool's functionality and parameter semantics adequately. It distinguishes from siblings and includes a helpful note. Missing details about error handling or partial VIN handling, but overall sufficient for a straightforward tool.

  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?

  Input schema has 0% description coverage, so the description must compensate. It adds meaning by stating that vin is a VIN (17 characters or partial) and that model_year improves accuracy, but it could provide more explicit constraints or format guidance for both parameters.

  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 'Decode a VIN into vehicle details (make, model, year, engine, plant, safety equipment)', specifying a verb and resource, and it distinguishes from sibling tools like check_vin_recalls and get_recalls which deal with different aspects.

  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?

  Description says 'Call this when the user provides a VIN... and wants to know what vehicle it is', giving clear when-to-use context. It also notes that passing model_year improves accuracy for pre-2001 VINs. However, it lacks explicit when-not-to-use guidance or alternatives.

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

• get_safety_ratingsA
  4/5.0
  Behavior4/5

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

  With no annotations, the description carries full burden. It discloses that results are per body-style variant, which is a behavioral trait. It does not mention destructive actions or auth needs, but the tool is a read-only lookup.

  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, highly concise, with the core action front-loaded. 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?

  While the output schema exists (not shown), the description lacks any mention of required input parameters. For a 3-param required tool with 0% schema coverage, the description should at least list the expected inputs.

  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 input schema has 0% description coverage for parameters. The description does not explain the parameters (make, model, model_year) or their formatting, leaving the agent to rely solely on parameter names. This is insufficient compensation for the low 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 clearly states it retrieves NCAP crash-test star ratings for a vehicle, specifying rating types (overall, frontal, side, rollover) and mentioning per-body-style variants. This distinguishes it from sibling tools like recalls or VIN decode.

  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: 'Call this when the user asks how safe a vehicle is or how it scored in crash tests.' It does not explicitly state when not to use, but the context signals and sibling names imply distinct use cases.

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

• check_vin_recallsA
  4.2/5.0
  Behavior3/5

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

  No annotations are provided. The description mentions chaining VIN decoding with recall search but does not disclose error handling, validity requirements, or any side effects. Adequate but could be more explicit.

  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, no redundancy. First sentence states the action, second provides an example use case. Every word earns 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?

  Given the tool has an output schema and is simple (one parameter), the description covers the essential behavior. Could mention error cases but overall complete.

  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?

  Only one parameter 'vin' with no schema description. The description adds minimal detail beyond the parameter name. Baseline 3 is appropriate since the parameter is self-explanatory.

  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 decodes a VIN and looks up recalls in one step. It distinguishes itself from sibling tools like decode_vin and get_recalls by combining both actions.

  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?

  Explicitly provides a use case: 'Call this when the user gives a VIN and asks does my car have any recalls?' and implies alternatives for separate decoding or recall lookup.

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

• get_recallsA
  4.2/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It indicates a read operation ('Get') and focuses on public safety data. However, it omits details like rate limits, error conditions, or what happens if no recalls are found. The basic behavior is clear but lacks depth.

  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 concise: two informative sentences plus an example. It front-loads the purpose, provides usage guidance, and is free of superfluous text.

  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 presence of an output schema (which documents return values) and sibling tools for context, the description sufficiently covers when and how to use the tool. It could briefly mention the output structure but is adequate for the complexity.

  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 0% coverage (no descriptions for parameters). The description adds value by providing an explicit example ('make="Honda", model="Civic", model_year=2020'), clarifying the expected format and use of each parameter.

  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 explicitly states 'Get NHTSA safety recall campaigns for a vehicle make/model/year', using a specific verb and resource. It clearly distinguishes from siblings like check_vin_recalls which uses VIN.

  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 provides explicit context: 'Call this when the user asks whether a vehicle has recalls, what a recall covers, or how a defect is remedied.' It includes an example but does not explicitly exclude other use cases or mention alternatives like check_vin_recalls.

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