Newyorkautoquotes

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered. The description adds useful context about the required credential (buyer key) and what data is returned, but does not disclose error behavior for missing/invalid keys or any rate limits. Given the strong annotation coverage, this is acceptable but not exceptional.

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?

A single sentence that front-loads the resource ('For a registered agency') and then compactly lists all returned fields, ending with the input requirement. There is no redundancy or filler, though the dash of field names could be slightly more structured.

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 enumerating the return fields thoroughly. The input requirement is stated, and the tool is a simple read-only status check, so no complex orchestrations are needed. It lacks edge-case behavior, but for a straightforward status retriever this is mostly sufficient.

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% — the schema already documents buyer_key as 'Issued to the licensed contact at confirmation.' The description reinforces this by mentioning the key requirement but adds no new semantic detail beyond the schema. Baseline 3 applies because the schema carries the burden.

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 enumerates exactly what data the tool returns ('states, rail, terms, founding period, queued and delivered counts, credit balance, and whether the licence was verified'), giving a specific clear resource. However, it uses no leading verb ('Gets', 'Returns'), relying on the title 'Agency status (buyers)' to convey the action. Sibling names like check_eligibility are not directly similar, so no explicit differentiation is needed.

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 states the prerequisite ('Requires the buyer key issued at confirmation'), which tells an agent the key is mandatory. It does not explicitly state when to use this tool versus check_eligibility or data_use_terms, but the status-read purpose is implied strongly enough by the detailed return list.

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 cover readOnly/idempotent/non-destructive, and the description adds useful context: it discloses what the call returns (licensed states, what can be done in each, and agent counts) and notes that no personal details are needed. This is meaningful behavior beyond the annotation flags.

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: purpose, ordering, and return contents. Front-loaded and tight, with every sentence earning its place and no redundant wording.

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 description covers purpose, sequencing, and return values, but it omits the optionality of the state parameter and what happens when state is omitted. It also does not connect to sibling tools beyond 'call first,' leaving small but real gaps for an agent deciding how to invoke it.

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 about 50%: 'state' has a description, while 'product' only has an enum with a default. The description clarifies the state parameter's role and broadens the output scope, but it does not mention that state is optional or explain product semantics; the schema's enum/default partially compensates.

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 action ('check whether we can return quotes') and a clear scope ('for a state'), with a temporal qualifier ('before any personal details are collected'). It is distinct enough but does not explicitly name any sibling tool, so it stops short of the strongest differentiation.

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 phrase 'Call this first' gives explicit sequencing guidance, and 'before any personal details are collected' provides a clear precondition. However, it does not mention alternatives or state when not to use this tool, so it lacks full routing information.

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 readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description does not need to repeat safety traits. It adds the machine-readable evaluation factor and notes the content (receivers, purpose, retention, revocation, consent wording), which goes beyond the schema. No contradictions. A 3 is fair because the description relies on annotations for the behavioral profile.

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 information-dense, front-loading the core meaning ('What happens to anything you send us') and ending with the operational cue. Only minor redundancy: 'exact consent wording' and 'Machine readable so you can evaluate' are both needed and neither is wasted.

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 there is no output schema, the description does a good job of conveying what response the agent can expect (data terms + consent wording, machine readable). It directly ties to the sibling tool, covering the main use case. It does not mention any auth or permission needs, but a read-only data-terms lookup likely requires none, so the gap is minor.

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 schema has zero parameters, so the description has no parameter semantics burden. It correctly spends the space describing what the tool returns and why it matters. Baseline for 0 params is 4.

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 clear verb ('evaluates') and specifies an explicit topic: the data-use terms and consent wording for what happens to a user's sent data. It names the exact sibling ('request_agent_contact') from which it should be used before, so it is well distinguished from siblings.

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 states when to use the tool ('before request_agent_contact') but does not explicitly state when not to use it or name alternatives for other purposes. Still, that timing guidance is actionable and contextual.

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 says that for unlicensed territories "the request is still registered and a quote_id returned," which describes a state-changing side effect, yet the annotations declare readOnlyHint=true. This is a direct contradiction: registering a request and returning a quote_id implies creating state, not a read-only operation.

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 sentences, each earning its place: the first states the core purpose, the second clarifies the minimal data requirement, and the third explains fallback behavior and error handling. No redundancy and the most important information 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?

With no output schema, the description adequately conveys the key return values: prices and a quote_id when unlicensed. It also covers missing-facts behavior. It could be more explicit about the exact response shape, but the essential behavioral path is complete for an 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 only 20%, so the description must compensate for most parameters. It usefully frames all inputs as "rating facts" and excludes PII, but it does not explain the meaning of several parameters like prior_continuous, violations_3yr, or annual_mileage beyond their self-explanatory names. This is partial compensation, not complete.

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 opens with a specific verb and resource: "Return indicative auto insurance prices from multiple carriers." It also clarifies the licensing condition, which distinguishes this from siblings like market_data or check_eligibility by stating exactly what kind of output is produced.

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 explains when prices will be returned and what happens when the entity is not licensed, plus it explicitly states that only rating facts are needed and that PII is not required. It does not explicitly name alternative tools, but the context is clear enough for an agent to decide 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?

Beyond the readOnlyHint and non-destructive annotations, the description discloses de-identification guarantees, the exact fields excluded, payment requirements, pay-per-record pricing, and the 402-plus-preview behavior when payment is missing. This is substantial behavioral detail and does not contradict 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?

Every sentence earns its place: dataset definition, de-identification, filters, pricing/payment, error behavior, and schema link. The most important scoping information is front-loaded, and there is no 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 an 11-parameter paid data product with no output schema, the description is unusually complete: it covers contents, exclusions, filters, pagination, payment methods, failure mode, and points to a sample/schema endpoint. An agent has enough to invoke it correctly.

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 only 36% schema coverage, the description compensates well by explaining filter semantics (state, since, until, door, vendor), pagination via limit/offset, and the payment credential options (prepaid buyer key, x402, MPP, AP2). It leaves concrete value formats unspecified, but points to /data.json for 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 states the exact resource (de-identified auto-insurance demand records) and the action (buy/pull the dataset), and enumerates the fields and outcome states inside it. This clearly distinguishes it from sibling tools like get_quotes or pull_requests.

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 clear operational context: use it to buy and filter demand records by state, date, door, and vendor, with limit/offset pagination and payment credentials. It does not explicitly name sibling alternatives or state when not to use it, so it misses the when-not guidance for a 5.

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?

Even with annotations declaring readOnly=false, destructive=false, and idempotent=false, the description adds meaningful behavior: the x402 payment requirement with status 402 and accepts[] until paid, plus the scope-projection guarantee ('never wider') and the queue-draining, non-idempotent nature. It does not contradict the annotations and adds context beyond them.

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 short sentences pack behavior, auth requirement, payment condition, and data-scope guarantee with no filler. The most important action is front-loaded in the first sentence.

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 pull operation with no output schema, the description adequately covers what the tool does, who may call it, the metered-payment 402 behavior, and the scope constraint. It would be stronger with an explicit statement about the response shape or records list, but the first sentence makes that reasonably inferable.

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 75%, and the description usefully explains that buyer_key is required and that payment is needed for metered buyers (status 402/accepts[]). However, it does not clarify when to supply mandate versus credential or how they relate to the x402 payment, leaving the three optional payment-related parameters ambiguous.

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 opens with 'Drain the queue of pool records routed to you,' naming a specific action and object, and the title 'Pull routed records (buyers)' reinforces it. This clearly separates it from sibling tools like get_quotes or market_data, which do not describe consuming a buyer-specific routed queue.

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 a clear use context: use this when records are routed to you as a buyer and you need to drain the queue. It also states prerequisites ('Requires your buyer key') and the conditional payment path for metered buyers, though it does not name exclusions or alternative tools.

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 a non-read operation, and the description adds substantial behavioral context: the licensed contact confirms by email link, nothing is delivered before confirmation, founding-period free leads, and prepaid credits by card. It also points to /join for terms, giving the agent a clear sense of prerequisites and side 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 purpose is front-loaded and each sentence adds useful context. The pricing sentence and /join reference are arguably extra but still relevant; the description is a bit dense but not bloated.

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 description covers the workflow, consent, confirmation, and lead delivery, which is strong. But with 11 parameters and no output schema, it leaves gaps around verify_token, webhook_url, website, and what the API returns after registration, so an agent may still be uncertain about optional fields.

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 description translates many required inputs into plain language, including NPN, licensed contact, states, and lead delivery method. However, it does not explain website, webhook_url, verify_token, or the 'pull by key' and SMS paths, and schema coverage is only 64%, so some parameters remain underspecified.

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 and resource: 'register the agency to receive consented consumer requests in its states.' It clearly identifies this as the onboarding/registration tool and distinguishes it from sibling tools like agency_status and request_agent_contact.

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 clearly scopes usage to 'an AI working for a licensed insurance agency' and explains the registration context. It does not explicitly name sibling alternatives or exclusion criteria, but the context strongly implies this is for initial buyer registration rather than status checks or lead pulling.

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 only provide readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description adds substantial behavior beyond this: consent provenance, scope-based sharing limits (up to 4 licensed agents vs marketing partners), the guarantee that nothing leaves without consent, receipting of deliveries/refusals, and POST /forget revocation. This is exactly the contextual disclosure an agent needs.

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 about five sentences, front-loads the core purpose, and every sentence adds operational value: prerequisites, consent, scope, privacy guarantee, and revocation. There is no filler or repetition.

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 9-parameter, no-output-schema tool, the description covers prerequisites, consent mechanics, scope limits, and revocation well. The only meaningful gap is that it does not state what a successful response contains or what the agent should expect back after invoking the tool.

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 only 44%, so the description must compensate. It does explain quote_id, full_name, phone_number, and the consent object including scope semantics and the consent URL. However, it leaves best_time, email_address, preferred_channel, verify_token, street_address, and evidence to the schema, so compensation is partial.

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 opens with a specific verb and resource: 'Connect the consumer with licensed insurance agents who can quote firm and bind.' It clearly names the needed inputs and even references get_quotes as the source of quote_id, which distinguishes it from sibling tools like check_eligibility or agency_status.

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 gives clear prerequisites: quote_id from get_quotes, consumer name and phone, and explicit consent. It implies this tool is used after get_quotes and before agent contact. It does not explicitly list when not to use it or compare against sibling alternatives, so it stops 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.