api-response-cost-analyzer

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

No annotations are provided, so the description must fully disclose behavior. It only says 'analyze' and 'estimate token count' without clarifying what 'analyze' entails, whether the tool is read-only, or what the output format looks like. The absence of return value details leaves significant ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no extraneous words. It efficiently conveys the core purpose, earning a high score for conciseness.

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

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

There is no output schema, so the description should explain what the tool returns. 'Estimate token count' vaguely implies a numeric output but does not clarify whether it returns just a number or additional analysis. Given the tool's simplicity, the lack of behavioral detail and absence of any error/edge-case context make it incomplete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds little beyond what the schema already provides; 'API response content' is not much more informative than the parameter description itself. No additional constraints or formatting details are given.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Analyze') and resource ('API response content') and specifies the outcome ('estimate token count'). It distinguishes itself from sibling tools like estimate_cost and optimize_payload by focusing on token counting rather than cost or optimization.

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

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

The description implies usage (when you need to estimate token count for API responses) but provides no explicit guidance on when to use this tool versus siblings like estimate_cost, which likely also deals with tokens. No alternatives or exclusions are mentioned.

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

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

With no annotations, the description carries the full burden for behavioral disclosure. It only states the comparison purpose, omitting any mention of side effects, permissions, output format, or how the parameters interact. This is similar to the update_drive calibration case, which scored 2.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, front-loaded with the core action, and contains no redundant words. Every word earns its place.

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

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

For a tool with no output schema and no annotations, the description is too sparse. It fails to explain what the tool returns, whether models is actually required to compare, or how content and input_tokens factor into the cost comparison. This leaves critical gaps for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (all three parameters have descriptions), so the baseline is 3. The description adds no extra semantic meaning beyond the schema, and its implication that 'models' is needed ('across multiple LLM models') slightly conflicts with the schema marking models as optional.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'compare' and clearly identifies the resource ('API response costs across multiple LLM models'). This distinguishes it from sibling tools like 'estimate_cost' (likely singular cost) and 'analyze_response' (likely content analysis).

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

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, no exclusions, and no naming of alternative tools. It is a bare functional statement.

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

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

With no annotations available, the description carries the full burden of disclosing behavioral traits. It states the core action ('estimate') but does not clarify whether this is a read-only calculation, what outputs are returned, any required permissions, or potential side effects. The behavior is under-disclosed for an agent to fully anticipate the tool's effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that immediately conveys the tool's purpose. It contains no filler or redundant information, earning the highest score for conciseness and front-loading.

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

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

The tool is relatively simple with 3 parameters, all fully described in the schema. Although there is no output schema or annotations, the description makes the return value implicit ('cost') and the invocation clear. It is nearly complete for this level of complexity, though it could be improved by explicitly stating the output format.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters clearly. The description adds no additional meaning beyond the schema, such as suggesting how 'input_tokens' interacts with 'content' or providing examples. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('estimate'), the resource ('API response cost'), and the specific scope ('for a specific LLM model'). This distinguishes it from sibling tools like compare_models, which likely handles comparisons, and analyze_response, which likely focuses on content quality.

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

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

No guidance is provided on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or references to sibling tools. The phrase 'for a specific LLM model' slightly implies a single-model context, but there is no explicit when-to-use or when-not-to-use guidance.

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

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

No annotations are present, so the description must disclose behavioral traits. It implies a read-only operation via 'Get' but does not explicitly state side effects, permissions, rate limits, or return format. The absence of an output schema makes this gap significant.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no wasted words. It efficiently conveys the core action, and while it is minimal, it is appropriately concise for the tool's simplicity.

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

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

For a simple one-parameter tool, the description provides the essential purpose but fails to disclose behavioral details such as read-only nature or what the suggestions contain. Since there is no output schema, the description should offer more context to be fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema fully describes the only parameter 'content' as 'API response content', achieving 100% schema coverage. The description adds no additional meaning, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function with a specific verb ('Get') and resource ('optimization suggestions'), and the purpose ('to reduce API response cost') adds context. It distinguishes from siblings like estimate_cost, which focuses on cost estimation rather than suggestions.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of context, preconditions, or exclusions, leaving the agent to infer when optimization suggestions are appropriate.

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