Server Quality Checklist

Tool Scores

• estimate_costA
  3.9/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden for disclosing behavior. It does not state whether the tool is read-only, makes external network calls, or uses live pricing data. While 'estimate' implies no actual API invocation, this is not explicit, leaving significant transparency gaps.

  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?

  The description is efficient: a single-sentence purpose followed by a concise Args list. It is front-loaded and not overly verbose. The minor redundancy with schema defaults (e.g., default values repeated) prevents a perfect score.

  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 low complexity and the presence of an output schema, the description is adequate for basic usage. However, it lacks guidance on when to choose this tool over siblings like compare_models_cost, and offers no behavioral notes (e.g., whether it requires network access). These are notable gaps.

  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?

  The schema has zero descriptive coverage, but the description compensates fully. It explains the 'text' parameter as 'input text/prompt', gives concrete model name examples, and clarifies 'expected_output_tokens' as a rough guess at response length with a default of 0 meaning input only. This 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 tool's function: 'Estimate the API cost of sending this text as input, plus optional expected output.' It uses a specific verb ('estimate') and resource (API cost), and distinguishes itself from sibling tools like count_tokens and compare_models_cost by focusing on cost estimation.

  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 clear context for when to use the tool: when an API cost estimate is needed for an input text and optional output length. However, it does not explicitly mention alternatives or exclusions relative to sibling tools, so it falls short of a 5.

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

• count_tokensA
  4/5.0
  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 of behavioral disclosure. It states that the tool counts tokens, which implies a read-only calculation, but does not explicitly mention whether it sends data externally, rate limits, or any side effects. The behavior is straightforward but minimally disclosed.

  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 front-loaded with a clear one-sentence purpose, followed by a concise Args list. Every element is relevant and there is no redundant or padded text. The structure is easy to parse.

  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?

  The tool is simple and has an output schema, so the description needn't explain return values. It covers the core purpose and parameter semantics, and even suggests a related tool (list_supported_models). It might benefit from mentioning the default model or clarifying that it's a non-mutating operation, but overall it is sufficiently complete for the tool's 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?

  Schema description coverage is 0%, so the description must compensate. It adds meaning to 'text' as 'Your text/prompt to tokenize' and clarifies 'model' with examples ('claude-sonnet-5', 'gpt-5') and a pointer to list_supported_models. This provides practical guidance beyond the bare 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 opens with a specific action and resource: 'Count how many tokens a piece of text would use for a given model.' This clearly distinguishes the tool from sibling tools like estimate_cost (which estimates cost) and list_supported_models (which lists model options).

  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 through its purpose but provides no explicit when-to-use or when-not-to-use guidance compared to sibling tools. The only hint is 'Call list_supported_models to see all options,' which addresses model selection rather than tool selection. There are no exclusions or alternative tool references.

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

• compare_models_costA
  4.3/5.0
  Behavior3/5

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

  With no annotations, the description carries the burden of behavioral disclosure. It discloses defaults for 'models' and the meaning of 'expected_output_tokens,' but does not mention whether this is a read-only operation, if it makes external API calls, or any limitations (e.g., pricing database dependence).

  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 and well-structured, starting with a one-sentence purpose followed by a simple Args list. No wasted words; 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?

  The tool has three parameters, an output schema exists, and the description covers all parameter semantics and the core purpose. It leaves little unexplained, though it could benefit from noting when to prefer this over single-model tools like count_tokens or estimate_cost.

  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 description coverage is 0%, so the description must compensate. It provides clear semantic meaning for all three parameters: text is the input, models is a list with a default behavior, and expected_output_tokens is a rough response-length guess applied uniformly.

  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 states 'Compare token count and estimated cost for the same text across several models,' which clearly identifies the tool's purpose with a specific verb and resource. This differentiates it from sibling tools like count_tokens (single model) and estimate_cost (single model).

  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 clearly implies when to use the tool: when comparing token counts and costs across multiple models. It does not explicitly mention alternatives or exclusions, but the purpose itself strongly signals the use case relative to the sibling tools.

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

• list_supported_modelsA
  4.3/5.0
  Behavior4/5

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

  With no annotations, the description carries the full burden. It adds value by specifying that only models with pricing data are included and that current rates are shown, clarifying the tool's read-only nature without needing explicit safety hints. It does not address potential quirks like pagination, but for a simple list operation this is adequate.

  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. Every word contributes to stating the tool's purpose and the content of the list, with no redundant information.

  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?

  Given the tool has no parameters and an output schema exists, the description is sufficient for an agent to select and invoke the tool correctly. It clearly conveys what the tool does and what data is returned.

  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 is 4. The description provides no parameter details, but none are needed; the mention of 'current rates' hints at the output structure without describing 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?

  The description clearly states the action (list), resource (models), and scope (models with pricing data and their rates). This distinguishes it from sibling tools like count_tokens or estimate_cost, which handle calculations rather than enumeration.

  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 this is the foundational tool for discovering available models, but it does not explicitly mention when to use it instead of siblings or provide exclusions. Usage guidance is implied rather than stated.

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