QuoteFirst

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

Description reveals that the tool triggers model execution and instructs to poll 'get_deliverable' for the result, indicating asynchronous behavior. Annotations are not contradicted: readOnlyHint=false aligns with mutation, destructiveHint=false is plausible.

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?

Two sentences with no filler, front-loading the core action and providing actionable follow-up. 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 simple action with no output schema, the description covers the main purpose and async behavior. However, it lacks details on prerequisites, side effects, or error states, which are partially mitigated by sibling tools and annotations.

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 0% (no parameter descriptions), and the description only mentions 'one quote on a request', implicitly referencing request_id and quote_id but not explaining their roles or format. This adds minimal value beyond the schema.

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 'accept' and the resource 'quote', distinguishing it from siblings like 'quote' (creating quotes) and 'get_deliverable' (retrieving results). It explicitly notes that the chosen model executes the task, adding context.

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 after a quote has been obtained but does not explicitly state when to use this tool vs. alternatives like 'quote' or 'rerun'. No exclusions or comparison with siblings is provided.

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?

Beyond annotations (readOnlyHint=true), the description adds that it's deterministic and doesn't use allowance. This provides helpful behavioral context not explicitly in annotations, though no contradictions.

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?

Two short sentences convey core purpose, key behavioral trait, and optional parameter. No redundant or ambiguous phrasing.

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?

Given no output schema, the description should at least hint at return format (e.g., estimated cost value, currency). It omits that, plus does not explain the required 'description' parameter. Still, it covers core usage adequately.

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 0% and description only mentions 'expected_output_words' optionally. The required 'description' parameter is completely unexplained, leaving the agent to infer its purpose. This is insufficient.

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 purpose: generating a deterministic cost estimate across all models for a task without using allowance. It distinguishes from siblings like quote by highlighting it doesn't use quota, and the verb 'estimate cost' is specific.

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 when to use this tool (when a quick, no-cost estimate is needed vs. quote tool for actual allowance use). However, it lacks explicit when-not-to-use or alternative tool mentions.

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?

The description adds specific context beyond the readOnlyHint annotation by enumerating the possible statuses (quoting, quoted, executing, completed, failed) and mentioning that deliverable is returned. This helps the agent understand the scope of the response.

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 extremely concise (two sentences) with no unnecessary words. The most important information (purpose and status enumeration) is front-loaded.

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 has a simple interface (1 required parameter, no output schema), and the description provides a basic purpose and status list. However, it lacks details about the return structure (e.g., how status and deliverable are presented), which is a minor gap given the tool's simplicity.

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?

With a schema description coverage of 0%, the description should compensate by explaining the 'request_id' parameter. It does not mention the parameter at all, leaving the agent to infer its meaning from the tool's purpose. This is insufficient for a required parameter.

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 purpose with a specific verb ('Get') and resource ('status and deliverable of a request'), and lists the possible statuses. This distinguishes it from sibling tools which are action-oriented (accept, quote, rerun) or estimation (estimate_cost).

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 explicit guidance on when to use this tool versus alternatives. The description only states what it does without indicating when it is appropriate or when to prefer other tools like 'spend' or 'estimate_cost'.

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?

Annotations are sparse (no read-only or destructive hints), so the description carries the burden. It discloses that the tool returns a request id, quotes, and recommendation, and that no work is done. However, it does not mention potential side effects like reserving resources or whether quotes are binding, leaving some 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.

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

Two efficient sentences, front-loaded with the core action and outcome. Every word earns its place with no redundancy or fluff.

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?

Given the tool's simplicity (single parameter, no output schema, sparse annotations), the description adequately covers what it does and its return. It could optionally mention whether quotes are binding or how prices are determined, but the current completeness suffices for basic usage.

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% with a single parameter 'description' documented as 'The task to be quoted'. The description adds no additional meaning beyond the schema, thus baseline 3 is appropriate.

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 submits a task description and receives dollar quotes from multiple frontier AI models. It specifies the return includes request id, quotes in cents, and a recommendation, making the purpose distinct from siblings like 'estimate_cost' or 'accept'.

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 indicates this is used 'before any work is done' to get quotes, implying a preparatory step. It does not explicitly exclude scenarios or mention alternatives, but the context of sibling tools and the precise description provide adequate guidance for when to use it.

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?

Annotations (readOnlyHint=false, destructiveHint=false) are consistent with description. Description adds context: it's free, produces fresh deliverable with same quote. No contradictions.

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?

Two sentences, no wasted words. Purpose and condition are front-loaded.

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?

Tool is simple with one param. Description covers condition and outcome. Missing edge cases (e.g., invalid request_id, score above 70) but adequate for basic use.

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 0%, but description does not add meaning for the single 'request_id' parameter beyond type string. It implies it refers to a completed request but gives no format or example.

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 purpose: to re-run a completed request for free if its deliverable scored below 70/100. It specifies the condition and outcome, distinguishing it from siblings like 'accept' or 'estimate_cost'.

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 specifies when to use (when quality check score is below 70/100). It lacks explicit exclusions or alternatives, but the condition is clear. Sibling tools provide context.

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?

Annotations already indicate read-only and non-destructive behavior. The description adds value by specifying the output format (cents, total and per model) beyond what annotations provide.

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, concise sentence that conveys all necessary information without unnecessary words.

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?

Given no output schema, the description adequately explains the return format and breakdown, making the tool's behavior fully understandable.

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?

No parameters exist, and schema coverage is 100%. The description adds no parameter details, which is acceptable for a parameterless tool; baseline 4 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 retrieves monthly spend in cents, both total and broken down by model, distinguishing it from sibling tools like accept, estimate_cost, etc.

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 monthly spend data is needed, but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites.

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