Oklahomaautoquotes

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds value beyond annotations by disclosing the authentication requirement (buyer key) and enumerating the return contents, which is especially useful since there is no output schema. It does not mention failure behavior, but given the strong annotation coverage, this is a minor gap.

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; the field enumeration is dense but purposeful. The purpose is front-loaded with the resource and the returned data. The passive 'For a registered agency: its...' construction is slightly awkward but does not cost clarity.

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 single-parameter, read-only tool with no output schema, the description compensates well by enumerating the return fields and stating the required credential. It does not specify the response format or error cases, but the combination of full schema coverage, safety annotations, and the detailed field list makes this largely complete for an agent to call 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?

Schema description coverage is 100% — the schema already describes buyer_key as 'Issued to the licensed contact at confirmation.' The description's phrase 'Requires the buyer key issued at confirmation' largely restates the schema rather than adding new meaning. No additional format, lookup, or sourcing details are provided, so the baseline 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 identifies the resource (a registered agency) and enumerates the exact data returned: states, rail, terms, founding period, queued/delivered counts, credit balance, and licence verification status. However, there is no explicit verb like 'get' or 'retrieve' — the fragment 'For a registered agency: its...' implies retrieval rather than stating it — and it does not explicitly differentiate from sibling 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 gives one concrete prerequisite — 'Requires the buyer key issued at confirmation' — which tells the agent what credential is needed to invoke the tool. However, it does not state when to prefer this tool over check_eligibility or data_use_terms, nor when not to use it, so the usage context 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?

Annotations already declare the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context: no personal details are collected, and the tool returns licensing scope, permitted actions, and agent counts. There is 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 sentences with no filler. The purpose and ordering come first, followed by a concise summary of the return values. 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 simple two-parameter read-only tool, the description covers invocation order, output contents, and the absence of personal-data collection. It slightly underspecifies whether an omitted state returns all states versus a single state, but the schema's optional state parameter makes this 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?

The schema documents state's format and product's enum/default, so the description does not need to repeat them. At 50% schema description coverage, the description does not compensate for the undocumented product parameter, but product is fully constrained to 'auto' with a default, limiting practical risk.

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 ('check') and resource (whether quotes can be returned for a state), and clarifies it happens before any personal details are collected. It also names the key output, making the tool's role unmistakable and distinct from quote-fetching 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 explicitly says 'Call this first,' so an agent knows this is the entry point. It also explains the prerequisite timing (before personal details are collected), though it does not name alternative sibling tools or provide 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds context that the tool returns machine-readable information to evaluate an exchange before committing, which is useful but does not disclose details such as response format or any external data dependencies. No contradiction exists.

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 sentence that packs in all key aspects: data recipients, purposes, retention, revocation, consent wording, and the evaluative purpose. It is front-loaded with the core question ('What happens to anything you send us') and avoids redundancy with annotations or schema.

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 zero parameters, no output schema, and annotations covering safety, the description is complete for the agent's invocation purposes. The only minor gap is that it does not specify the exact return format or field names, but with no output schema and a natural-language description, this is acceptable. The tool's role in pre-contact consent evaluation is fully conveyed.

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 0 parameters, and schema coverage is 100% because the schema is empty. The description fully context matters because there are no parameters to document, and it clearly communicates the tool returns data-use information, which is the primary semantic needed for 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 description uses a specific verb ('evaluate the exchange') and resource (data use terms and consent wording) and clearly enumerates the content: recipients, purposes, retention, revocation, and consent wording. It distinguishes itself from siblings by explicitly mentioning request_agent_contact, making its purpose unmistakable.

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 says to use this tool when needing to know data handling terms and consent wording before presenting request_agent_contact. It implies the prerequisite use case (before making an exchange) but does not explicitly contrast with all sibling tools; however, the explicit reference to a sibling provides near-complete usage 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?

Annotation Contradiction: readOnlyHint=true indicates the tool does not modify state, but the description says 'the request is still registered and a quote_id returned so licensed agents can quote it,' which implies a persistent side effect. This directly contradicts the read-only annotation and is a serious inconsistency for an agent relying on the tool's safety 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 four sentences, front-loaded with the main purpose, and every sentence adds useful information: what it returns, what it does not require, unlicensed fallback behavior, and server behavior on missing facts. There is no repetition 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?

The description covers purpose, PII exclusions, unlicensed fallback, and missing-facts behavior. However, there is no output schema, and the successful licensed-path response format is only described as 'prices' without details. With 10 parameters and a contradictory read-only annotation, the description is not fully sufficient for an agent to confidently predict all invocation outcomes.

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%, and the description does not compensate. It explains that only rating facts are needed and lists excluded PII, but it does not clarify the meaning of fields like annual_mileage, violations_3yr, years_licensed, prior_continuous, or vehicle_make_model beyond their names. The 'server asks for exactly what it needs' note reduces some upfront burden, but the description still leaves most parameter semantics unexplained.

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 verb and resource: 'Return indicative auto insurance prices from multiple carriers.' It also clarifies the licensing boundary and the fallback of returning a quote_id, which distinguishes this from simple quote retrieval. This is specific enough for an agent to understand what the tool does and how it differs from sibling tools like 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 gives clear context: use this for indicative prices using only rating facts, with no PII required. It also explains the behavior when the entity is not licensed. However, it does not explicitly name sibling alternatives or state when not to use this tool, so the agent must infer the boundary from 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?

Even with readOnlyHint=true and destructiveHint=false already present, the description adds substantial behavioral detail: de-identification guarantees, explicit PII exclusions, a 402 behavior when unpaid, pricing by record, and the existence of a sample/schema endpoint. This gives the agent a realistic model of what will happen before calling the tool.

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 long but information-dense, covering dataset contents, PII exclusions, filtering, payment, failure behavior, and a sample endpoint in a logical order. Every sentence contributes useful guidance, though some phrasing such as 'people and their AI assistants made here' could be trimmed without losing meaning.

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 covers what an agent needs: data contents, privacy safeguards, filtering, payment methods, and the no-payment response. It does not spell out the exact response shape or date formats, but it points to /data.json for the schema and sample, which fills the remaining 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 description coverage is only 36%, so the description must compensate. It does so by enumerating the filter parameters (state, since, until, door, vendor) and pagination parameters (limit, offset), and by mapping payment methods to the relevant parameters (buyer_key, x402, MPP, AP2). It does not specify date formats or door/vendor value constraints, but it adds meaning well beyond the bare schema names.

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 it exposes the de-identified demand dataset of auto insurance requests, with a specific list of fields and outcomes. It is distinct from siblings like pull_requests or get_quotes because it positions this as the purchasable market dataset. The title and verb 'Buy' plus 'the demand dataset' make the resource unmistakable.

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 operational context: filter by state/since/until/door/vendor, paginate with limit/offset, and pay via buyer key, x402, MPP, or AP2. It also explains that without payment the tool returns a 402 with price and preview count, so an agent knows how to handle the no-payment case. It does not explicitly name alternative sibling tools or say when not to use them, 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.

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

Beyond the minimal annotations, the description discloses queue-draining/consumptive behavior, the x402 payment requirement with status 402 and accepts[], and the scope-projection guarantee ("never wider"). It also states the auth prerequisite ("Requires your buyer key"), all of which are behavioral traits not visible from annotations alone.

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: the core action is front-loaded, followed by the requirement, the payment caveat, and the security scope note. Every sentence contributes distinct 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?

It covers what the tool does, auth requirements, payment behavior, and scope projection, which is substantial for a tool without an output schema. The return payload shape is not explicitly described, but "drain the queue of pool records" strongly implies the records are returned. This leaves only a small 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?

The schema already documents 75% of the parameters (mandate, payment, credential), and the description only adds that buyer_key is required. The payment-flow explanation gives context but does not map parameters to payload fields or add meaningful semantics beyond what the schema descriptions provide. This matches the baseline for high schema coverage.

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 "drain" acting on "the queue of pool records routed to you," making both the operation and resource unmistakable. The title "Pull routed records (buyers)" reinforces this and sets it apart from sibling tools like agency_status and market_data.

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 context: it is for buyers pulling records routed to them, and it states the buyer_key prerequisite and the condition for metered buyers receiving a payment requirement. It does not explicitly name alternatives or say when not to use the tool, 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.

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

The description adds meaningful behavioral context beyond the annotations: the licensed contact must confirm via emailed link, nothing is delivered before confirmation, and billing shifts from a free founding period to prepaid credits. It also points to /join for terms. No contradiction with the readOnly/idempotent/destructive hints.

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 the sentences cover the workflow, required fields, confirmation, and funding—no filler. It is a little long, but each sentence adds operational value.

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 registration tool with no output schema, the description covers prerequisites, required inputs, confirmation behavior, and next-step terms, which is strong. It still leaves some conditional wiring (e.g., webhook URL when webhook delivery is chosen) and the immediate response format 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?

With 64% schema coverage, the description compensates by naming the required data: agency name, NPN, NPN state, appointed states, contact name/email/mobile, and lead-delivery method. It also attaches meaning to the email parameter (confirmation link) but leaves conditional details like webhook_url and verify_token mostly to 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 opens by naming the exact action ('register the agency') and its purpose ('to receive consented consumer requests in its states'), so an agent knows what the tool accomplishes. The title repeats the resource ('insurance agency as a buyer') but the description goes further and differentiates this from sibling tools like request_agent_contact 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?

It states the intended caller/context: an AI working for a licensed insurance agency, and it makes clear the registration prerequisite (licensed contact must confirm by email). It does not explicitly contrast with sibling tools or state when not to use it, so routing is mostly left to the agent's judgment.

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 meaningful behavior beyond annotations: up to 4 licensed agents receive request and contact details, sell_identity extends sharing to marketing partners, nothing leaves without consent, delivery/refusal is receipted, and POST /forget revokes. This is useful privacy and side-effect context. There is no contradiction with 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 efficient: purpose in the first sentence, prerequisites and consent mechanics in the second, and recipient/revocation behavior in the third. The repetition of the consent requirement is acceptable given the sensitivity of the operation. It errs slightly on length, but each clause contributes value.

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 complex tool with no output schema, the description covers consent flow, scoping, recipient limits, receipting, and revocation. The main gaps are the absence of a return-payload description and lack of detail on optional contact-channel parameters. Still, the agent has enough to select and invoke the 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?

Schema description coverage is low (44%), so the description compensates by explaining consent modes ('granted by the human, presented by you, or confirmed... at the consent URL'), the provenance of quote_id, and the meaning of the scope enum. It does not describe optional fields like best_time, email_address, or preferred_channel, but the required fields are well covered.

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 outcome: 'Connect the consumer with licensed insurance agents who can quote firm and bind.' It also identifies the required source of the quote_id ('from get_quotes'), which helps distinguish this tool from siblings like get_quotes or register_agency. The title reinforces the consent requirement without ambiguity.

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 states prerequisites: quote_id, full_name, phone_number, and explicit consent. It also explains scope behavior for contact_consumer versus sell_identity, giving the agent actionable routing guidance. It does not explicitly name an alternative tool to use instead, but the dependency on get_quotes and the consent condition provide adequate usage context.

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