Nevadaautoquotes

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 agent knows this is a safe read. The description adds useful behavioral context by requiring the buyer key and scoping the operation to registered agencies. It also enumerates the returned fields, which is the main expected behavior of a status endpoint.

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 two sentences: the first lists the payload, the second gives the required credential. Every clause earns its place and the key requirement is front-loaded near the field list.

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 one-parameter tool with no output schema, the description carries the burden of explaining what the agent receives, and it does so with an explicit field list. It does not cover error behavior, empty states, or exact value formats, but those are minor for a simple status read with strong 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?

The sole parameter buyer_key is fully documented in the schema as 'Issued to the licensed contact at confirmation,' and the description restates this same detail. Since schema coverage is 100% and the description adds no new format, source, or validation information, it meets the baseline but does not exceed it.

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 identifies a clear resource (a registered agency) and enumerates the specific data it returns: states, rail, terms, founding period, queued/delivered counts, credit balance, and verification status. The title 'Agency status (buyers)' plus the buyer_key requirement helps separate it from registration or eligibility siblings. It lacks an explicit imperative verb like 'get' or 'retrieve,' but the intent is unambiguous.

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 states a clear precondition: the agency must be registered and the caller must hold the buyer key issued at confirmation. This implies it is used after registration rather than during eligibility checks or initial registration. It does not name alternatives such as status_dashboard, so exclusion guidance is absent.

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 safety (read-only, idempotent, non-destructive), and the description adds useful behavioral detail: it returns licensed states, per-state capabilities, and agent counts. No contradictions or hidden side effects are implied.

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 tight sentences: purpose, when to call, and return contents. No filler, front-loaded, and every clause contributes information.

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 read-only eligibility check with supportive annotations and a minimal schema, the description covers purpose, timing, and output. A minor gap is that optional state semantics are not spelled out, but the schema signal partly covers this.

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 documents state and product partially; the description references state but does not clarify what happens when state is omitted or how product affects the response. Product is self-explanatory via its enum/default, but the optional-state behavior is left implicit.

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?

States a specific verb ('Check whether we can return quotes') and a clear resource (state eligibility), and distinguishes itself from quote-gathering tools by noting it runs before personal details are collected. Also describes concrete outputs: licensed states, allowed actions, and agent counts.

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?

Explicitly says 'Call this first,' giving clear workflow placement as a preflight check. It does not name sibling alternatives or state when not to use it, but the timing and purpose are unambiguous.

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, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the machine-readable output and the consent-evaluation purpose, but it does not disclose return format details; that is acceptable given the annotations carry most of the burden.

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 dense sentence that front-loads the resource, then efficiently enumerates the content list with a colon and commas. Every phrase adds information, and no space 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?

For a zero-parameter informational lookup with annotations covering safety, the description tells an agent what it will receive and when to call it. It stops short of specifying the exact response shape, but 'machine readable' plus the enumerated categories is sufficient for this 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?

The tool has zero parameters and 100% schema coverage, so there is nothing for the description to add. Per the baseline for 0-parameter tools, this is correct.

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 resource ('anything you send us') and enumerates the exact content categories the tool returns: recipients, purpose, retention, revocation, and consent wording. It also distinguishes itself from siblings by explicitly tying its output to request_agent_contact, so an agent can tell it apart from the other data-lookup tools.

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 signals when to use it: before request_agent_contact, to prepare consent wording and evaluate the data exchange. It does not explicitly name alternatives or state when not to use it, but the targeted reference to a sibling tool gives clear contextual routing.

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 annotation, the description adds meaningful behavior: no PII is collected for indicative pricing, unlicensed requests are still registered and yield a quote_id, and the server asks for only the missing facts. These are non-obvious behaviors an agent needs to know and are not present in the schema or 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 reasonably concise and front-loaded with the core purpose. All four sentences carry useful information, though the unlicensed-agent explanation could be tightened slightly.

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 10 parameters, 20% schema coverage, and no output schema, the description is not fully complete. It does mention the key quote_id outcome and the licensing caveat, but it leaves many parameter meanings and the overall response shape unspecified. It is adequate but has clear gaps.

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 undocumented parameters. It groups inputs as 'rating facts only' and clarifies that PII is not needed, but it does not explain most parameters such as annual_mileage, violations_3yr, years_licensed, or prior_continuous. The server-asks-for-missing-facts note does not help an agent supply correct values up front.

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 returns indicative auto insurance prices from multiple carriers, with a licensing condition. This is a specific verb+resource, but it does not explicitly differentiate from siblings like market_data or check_eligibility, so it misses the highest bar.

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 usage context: it takes rating facts only, does not require PII, and handles unlicensed situations by returning a quote_id for licensed agents. However, it does not name alternative tools or state when not to use this one, so exclusion guidance is implied rather than explicit.

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 substantial behavioral detail beyond annotations: it explains the de-identification guarantees, pricing model, minimum per pull, accepted payment methods, and the 402 response with a price and preview count when no payment is provided. This is exactly the kind of non-obvious behavior an agent needs to know.

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 adds value: dataset contents, privacy guarantees, filtering, pricing, payment, failure response, and a link to the sample/schema. The most important information is front-loaded, and there is no redundant 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 tool with 11 parameters, no output schema, and payment-related behavior, the description covers the critical invocation details: what data is returned, how to paginate, how to pay, what happens without payment, and where to find the full schema and sample. Nothing essential is missing for a competent agent to call this tool 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 schema description coverage at only 36%, the description compensates by mapping filter dimensions (state, since, until, door, vendor, limit, offset) and payment mechanisms (buyer_key, x402, MPP, AP2) to parameter groups. It does not spell out date formats or allowed values, but it gives enough semantic grounding for correct invocation.

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 title and description clearly identify the resource as 'de-identified demand records' and the operation as purchasing/retrieving that dataset. The description specifies exactly what the dataset contains and how it is filtered, distinguishing 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?

The description provides clear context for when to use this tool: when the agent needs de-identified auto-insurance demand records, with filtering by state/date/door/vendor and payment. It does not explicitly name alternative tools or state when not to use it, but the usage context is unmistakable.

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 substantial behavior beyond the annotations: it requires a buyer key, explains the x402 payment flow with status 402 and accepts[], and commits to scope projection. These are context-rich disclosures about authentication, payment gating, and data visibility. No contradiction with readOnlyHint=false or destructiveHint=false is present.

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: the core action appears first, followed by prerequisites, payment behavior, and a data-scope guarantee. Every sentence carries distinct information, and 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?

The description covers the main workflow: auth, payment prompting, and scope projection. However, there is no output schema, and the description does not describe the success response shape or behavior for an empty queue. Since the agent must handle the response, this is a moderate completeness gap.

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 descriptions cover mandate, payment, and credential, but buyer_key has no schema description. The tool description compensates by stating "Requires your buyer key," giving the required parameter clear meaning. It also ties the payment flow to the payment/credential parameters. The mandate parameter still relies solely on its schema description, preventing a perfect score.

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: "Drain the queue of pool records routed to you." This clearly distinguishes pull_requests from sibling tools like register_agency or market_data by emphasizing buyer-routed pool records. The title and description together make the tool's function unambiguous.

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 the tool: when you have pool records routed to you and possess a buyer key. It also describes the metered-payment condition. However, it never explicitly contrasts this tool with alternatives or states when not to use it, so guidance is more implied than directive.

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 discloses important behavioral traits beyond annotations: the licensed contact must confirm via an emailed link, nothing is delivered before confirmation, and the first agencies in a state receive free leads for a founding period. The annotations already indicate a mutating, non-idempotent operation, and the description complements them with activation and billing context. It does not explicitly state that registration is irreversible or that duplicate NPNs may be rejected, but it is reasonably transparent.

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 dense paragraph with a clear logical flow: purpose, required data, confirmation behavior, and billing terms. It front-loads the core registration concept and audience, then moves through the required fields and delivery options. It is slightly long but every sentence earns its place because there is no structured field-level documentation for several parameters.

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 11 parameters, 6 required, no output schema, and a mutating non-idempotent operation, the description covers the registration flow, prerequisites, delivery modes, confirmation step, and billing. It does not fully document the optional website and webhook_url parameters, nor does it explain pull-by-key mechanics. The reference to /join for terms is helpful. An agent could likely call this tool correctly but might need to ask about webhook URL and pull-by-key details.

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 64%, so some parameters are already documented in the schema (npn, contact_name, contact_phone, states, verify_token). The description adds meaning by grouping the required fields and explaining the delivery options and confirmation step. However, several parameters like website and webhook_url have no schema descriptions and are not fully explained in the description either; the description says 'how leads should arrive (email, webhook, or pull by key)' but does not clarify the relationship between verify_token, webhook_url, and pull mode. It does not sufficiently compensate for the undocumented 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 title and description clearly state the verb ('register') and resource ('an insurance agency as a buyer'), and specify what the registration enables: receiving consented consumer requests in its states. The description also includes the audience ('for an AI working for a licensed insurance agency'), which helps distinguish this from other agency-facing tools.

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 explains the registration flow and prerequisites implicitly: the agency must be licensed, have a producer NPN, be appointed in states, and provide a licensed contact who confirms by email. It does not explicitly name sibling alternatives or when to use them, but it does point to '/join' for terms, which is additional guidance. A clear when-to-use statement is missing, but the context is strongly implied.

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 generic hints, but the description discloses meaningful behavior: nothing leaves without consent, up to 4 agents receive contacts under default scope, marketing partners are possible under sell_identity, deliveries/refusals are receipted, and POST /forget revokes. This goes well beyond the structured fields and gives the agent important side-effect awareness.

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 adds value: purpose, prerequisites, scope behavior, consent boundary, and lifecycle/revocation. It is front-loaded with the core action and does not repeat schema field names mechanically.

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 tool with nested consent and no output schema, the description covers the most important operational context: prerequisites, consent, scope, receipts, and revocation. It does not elaborate on optional contact fields or the exact response shape, but the supplied context is sufficient for safe invocation in most agent flows.

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 explains quote_id, name, phone, and consent, but leaves parameters like best_time, email_address, and preferred_channel without additional meaning in the description or schema. The consent object is clarified well, but several parameters remain undocumented.

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: "Connect the consumer with licensed insurance agents who can quote firm and bind." It clearly separates this tool from siblings by anchoring it to quotable/bindable agent contact and explicitly requiring consent. The title reinforces the consent requirement without relying on it.

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 names the required prerequisite (quote_id from get_quotes), the required consumer approvals, and how consent can be obtained. It explains the two scope variants and their different sharing targets, but does not explicitly name sibling tools to exclude or state when not to use this tool.

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