BuyerAware

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

Annotations already signal non-read-only, non-idempotent, non-destructive, open-world behavior, and the description adds important context beyond those: it takes 1–2 minutes, returns a job_id to be polled, and costs 1 credit. This gives the agent a clear model of the side effects and latency without contradicting the annotations.

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 dense but every sentence earns its place: verdict purpose, output summary, input guidance, async behavior, cost, and sibling routing. It is front-loaded with the primary purpose and ends with the alternative-tool pointer, making it easy to scan.

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?

With no output schema, the description compensates by naming the return flow (job_id, then poll get_verdict), the main verdict fields, the credit cost, and the free alternative. An agent has enough to invoke the tool correctly and understand what will happen next.

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 a baseline of 3 is warranted, but the description adds crucial semantics: it groups listing_url, listing_text, and vin as alternative sources, states that at least one is required, and explains that providing more context sharpens the verdict. This materially helps an agent choose and combine parameters beyond what the schema states.

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?

Begins with a specific action and resource: 'Start a BuyerAware pre-purchase verdict on ONE used vehicle.' It enumerates concrete outputs (risk score, verdict, negotiation script, alternatives) and explicitly distinguishes itself from tank_alternatives and get_verdict, so an agent can separate it from sibling tools without inspecting their schemas.

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?

Provides explicit usage conditions: use it for a single used-vehicle pre-purchase verdict; supply at least one of listing_url, listing_text, or vin; more context improves accuracy. It also names the alternative tool for a different intent ('what should I buy instead' → tank_alternatives) and the follow-up step (poll get_verdict).

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 annotations are all false, so the description carries the full burden of disclosing side effects. It reveals that this operation issues a key, provides 3 free verdicts, and enforces a one-trial-per-email policy, which is meaningful behavioral context beyond the schema and annotations.

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 three sentences with no filler. It fronts the core purpose, then gives actionable next steps, and ends with the key exclusion rule. Every sentence 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, the description still tells the agent what to do with the result ('show the user the key and tell them to save it'). It also covers the prerequisite, the quota, and the uniqueness constraint, so nothing essential is missing.

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 both parameters and their meanings. The description adds workflow context but no additional parameter-level detail, matching the baseline for fully documented schemas.

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 states a specific verb ('Create'), a specific resource ('BuyerAware API key'), and the purpose ('so this user can run verdicts'). It clearly distinguishes this from the sibling tools, which are about analysis and lookup rather than key creation.

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?

It gives explicit instructions on when to call ('once when no key is configured') and when not to call ('Do not call it again for someone who already has a key'). It also explains the one-trial-per-email constraint, leaving no ambiguity.

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 declare readOnly, idempotent, and non-destructive. The description adds valuable behavioral facts beyond annotations: the asynchronous job statuses (queued/running) and the free polling guarantee. No contradiction with annotations.

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 compact sentences: the first states the purpose, the second gives the polling condition and a cost differentiator. Every phrase earns its place with no 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?

For a simple 1-parameter polling tool with no output schema, the description explains the polling loop, expected statuses, and cost implications. Nothing an agent needs to call it correctly is missing.

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%, and job_id is already described as 'the job_id returned by analyze_listing'. The description repeats the origin of job_id without adding new format or type information, so it stays at the baseline.

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 states the specific action 'Fetch the result of an analyze_listing job' with a clear verb and resource. It distinguishes itself from sibling analyze_listing by naming it as the job producer, leaving no ambiguity about what this tool returns.

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?

Provides explicit polling behavior: if status is 'queued' or 'running', wait ~15 seconds and call again. It doesn't explicitly contrast with alternatives, but the direct reference to analyze_listing makes the intended use case clear.

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 declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context beyond annotations: no API key needed, free, instant answers, and a curated data source.

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?

Three concise sentences, front-loaded with the most useful information, with no redundancy. Every sentence contributes distinct value: cost/auth, purpose, and routing to the alternative.

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 recommendation tool with one required parameter and no output schema, the description covers the key invocation context: data source, budget relevance, use case, and sibling differentiation. It does not detail response shape, but the stated purpose strongly implies the output.

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 documents all parameters. The description adds little parameter-level meaning beyond mentioning budget and a typical 2-3 result count, which matches the default limit.

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 'Recommend' with a clear resource: lower-risk used vehicles ('Tanks') from BuyerAware's curated 47-platform database near a budget. It distinguishes itself from analyze_listing by explicitly contrasting the use case.

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?

It provides explicit when-to-use guidance: 'Use when a buyer asks what to buy instead, or what is reliable for $X.' It also names the alternative: 'Use analyze_listing instead when they have a specific car in mind.'

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 establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context beyond them: no API key needed, instant responses, curated-database coverage limits, and the important no-match-is-a-signal behavior. This goes beyond what annotations alone convey.

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 compact and front-loaded with key decision-relevant facts: free, instant, single-platform lookup, coverage scope, and follow-up behavior. Every sentence adds useful information without redundancy or filler.

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 2-parameter lookup with annotations covering safety, the description is largely complete: it lists the returned data, defines coverage, and explains the no-match case. The absence of an output schema is partially compensated by the output list, but exact return formatting or field structures are left unspecified.

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 baseline is 3. The description reinforces that mileage is used for valuation ('private-party value at a mileage') but does not add new constraints, formats, or clarifications beyond the schema. It provides no extra semantic value for the parameters.

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 and resource: 'Look up one platform in BuyerAware's curated database' and enumerates the returned outputs (risk score, verdict, trap, value, cost). It distinguishes itself from siblings by explicitly defining its 47-platform scope and by noting that no match should be followed up with analyze_listing.

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?

It clearly gives context for when to use the lookup and explicitly names analyze_listing as the follow-up for platforms outside the 47 covered. It does not explicitly exclude get_verdict or tank_alternatives, but the lookup-vs-listing distinction is still clear enough for an agent.

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