Californiacarquotes

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

Annotations already declare readOnly/idempotent/non-destructive, so the safety profile is covered. The description adds that the buyer key is required and lists the read payload, but doesn't explain edge cases such as unverified licences or expired keys.

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?

One sentence front-loads the resource and lists the fields compactly; the key requirement is a second short sentence. There is no filler or redundant explanation.

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 read-only tool this is nearly complete: it states the required credential and all major return groups. The lack of an output schema is mitigated by the explicit field list. Slight ambiguity in terms like 'states' and 'rail' prevents a 5.

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 describes buyer_key as issued to the licensed contact at confirmation; the description repeats this confirmation requirement. It adds no operational detail beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The title and description identify the resource ('agency') and the operation (return status details). It enumerates the returned fields (states, rail, terms, counts, credit balance, licence verification), but it never names a sibling or states what it is not, so differentiation is absent.

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 only guidance is 'For a registered agency' and the requirement of the buyer key; there is no explicit when-to-use, when-not-to-use, or mention of alternatives such as check_eligibility or register_agency.

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, covering the safety profile. The description adds value beyond that by explaining what the check returns (licensed states, per-state capabilities, licensed agent counts), giving the agent a clear picture of the tool's behavior 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?

Two sentences with no filler. The primary purpose is front-loaded, followed by the call-ordering instruction and a concise summary of return values. Every clause 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?

The tool is simple (2 params, no output schema), and the description covers purpose, call ordering, and return shape (licensed states, capabilities, agent counts). Nothing an agent needs to call it correctly is missing; the description compensates fully for the lack of an output schema.

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 50%: state has a clear description, and product is constrained to a single enum value with a default, so it is self-explanatory. The description adds minimal parameter-specific meaning, only tying 'state' to the notion of checking a state. This is adequate but does not go beyond the schema in any meaningful way.

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 ('Check whether') with a clear resource ('we can return quotes for a state') and adds a distinctive scope: 'before any personal details are collected.' This clearly separates it from sibling tools like get_quotes, which presumably performs the actual quote return after eligibility is confirmed.

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?

'Call this first' is explicit, direct guidance on when to invoke this tool relative to other steps. The phrase 'before any personal details are collected' further clarifies the usage window. It does not name alternatives or state when not to use it, but the sequencing instruction is strong and 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 communicate readOnlyHint, idempotentHint, and non-destructiveness. The description adds value beyond annotations by disclosing what information the tool surfaces (data recipients, purpose, retention, revocation, consent wording) and noting that output is machine-readable for evaluating an exchange before committing.

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 dense sentences with no filler. The opening clause front-loads the core purpose, the parallel list efficiently enumerates the covered terms, and the final sentence adds the machine-readable evaluative benefit without redundancy.

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 partially compensates by stating the result is machine-readable, but it does not specify the exact format. Given zero parameters and safety annotations, an agent can still 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?

This tool has zero parameters and the schema covers 100% of them, so no parameter explanation is needed. The description appropriately focuses on purpose and output semantics, meeting the baseline for a no-parameter tool.

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 precisely what the tool provides: data use terms and consent wording, naming the specific content (recipients, purpose, retention, revocation) and the sibling tool it is meant to precede (request_agent_contact). It is specific, non-tautological, and clearly distinguishes itself from the sibling list.

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 anchors usage timing: present this consent wording before request_agent_contact. It does not list exclusions or alternative tools, but the context is clear enough for an agent to know when this tool is relevant.

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 'the request is still registered and a quote_id returned,' which implies creating a persistent record/ID, while annotations declare readOnlyHint=true. This is an annotation contradiction; the tool cannot be read-only if it registers requests and returns a new quote_id.

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?

Four focused sentences, each earning its place: main purpose, PII exclusion, unlicensed fallback, and server behavior. Information is front-loaded and there is no redundant phrasing.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the main output, licensing edge case, and error/request behavior, which is strong for a 10-parameter tool with no output schema. It still leaves the license-status success response format somewhat implicit, but the essential call and fallback semantics are present.

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. It usefully clarifies that only rating facts are needed and enumerates excluded PII, but it does not explain the optional fields (annual_mileage, violations_3yr, prior_continuous, etc.) or their roles, so the gap is only partially filled.

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 ('Return'), a concrete resource ('indicative auto insurance prices'), and scope ('from multiple carriers, where this entity is licensed to show them'). This clearly distinguishes it from eligibility or agency-status 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?

Conveys when to use it: when an indicative price is needed and only rating facts are available. It also explains the unlicensed-carrier fallback, but it does not explicitly name alternatives or state when not to use it, leaving some inference to the agent.

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 indicate readOnly=true and destructive=false, and the description reinforces this by describing a read-only purchase of data. It adds meaningful behavioral context beyond annotations: the pricing requirement, the 402 response without payment, the de-identification guarantees, and the availability of a sample/schema. The only minor gap is not describing pagination behavior or response structure in detail, but the description discloses the important failure mode.

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 dense, front-loaded paragraph that conveys the dataset composition, privacy guarantees, filtering options, pricing model, payment methods, and failure behavior in a few sentences. Every sentence adds operational value. The only minor inefficiency is the slightly long list of fields, but it is necessary to convey what the dataset contains.

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 read-only data access tool with 11 optional parameters and no output schema, the description covers the key operational aspects: what data is returned, privacy exclusions, filtering, pagination via limit/offset, payment requirements, error behavior (402), and a pointer to the sample/schema. It could be more complete by explaining how the 'mandate' and 'credential' parameters interact with the payment methods, and by describing the exact response format, but the essentials are present.

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 for undocumented parameters. It does mention several parameters (state, since, until, door, vendor, limit, offset) and payment methods (buyer_key, x402, MPP, AP2), which maps to some of the 11 parameters. However, parameters like 'mandate' and 'credential' are only minimally described in the schema and not explained in the description; an agent might not know exactly how to construct the payment payload. The description adds value but does not fully compensate for the low 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?

Description clearly states the tool returns a de-identified demand dataset of auto insurance requests, enumerates the fields, and explicitly emphasizes what is excluded (names, emails, five-digit ZIPs). The title 'Buy de-identified demand records' and description together distinguish this from siblings like get_quotes or pull_requests by focusing on the historical dataset rather than quotes or pulls.

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 explicit context: users can filter by state, since, until, door, vendor; limit and offset; and pay via prepaid buyer key, x402, MPP or AP2. It also states what happens without payment (402 with price and preview count) and where to find a free sample. It doesn't explicitly say when to prefer this over siblings, but the dataset scope is clearly defined enough for an agent to infer appropriate use.

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 provide readOnlyHint:false and destructiveHint:false, so the description need not repeat safety basics. It adds valuable behavior: queue draining semantics ('Drain the queue'), the x402 payment requirement with status 402 and accepts[], and the projection constraint ('never wider' than granted scope). These are meaningful beyond 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?

Three sentences: the first gives the action, the second adds the payment requirement, the third adds scope projection. Each sentence earns its place, and the most important information is front-loaded. Slight room for improvement would be explicitly tying the second sentence to the payment/mandate/credential parameters, but it is compact and purposeful.

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 four parameters and no output schema, the description covers the key behavior: what is drained, who can use it, when payment is required, and how scope is bounded. It does not describe the return format or the exact mechanics of supplying payment, but that may be covered by the payment/credential parameter descriptions and the queue-draining framing is 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 75%, so the schema already documents mandate, payment, and credential. The description adds context for payment (metered buyers receive status 402 until payment supplied) and buyer_key is required. The credential parameter's base64url format is in the schema. The description does not need to restate all parameter details; 3 is the baseline given 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 opens with a specific verb and resource: 'Drain the queue of pool records routed to you.' It clearly identifies the tool as a pull/consume operation for routed records, distinct from siblings like check_eligibility or get_quotes. The title 'Pull routed records (buyers)' reinforces the action and audience.

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 tool is for pulling records routed to the caller as a buyer, and it mentions the x402 payment condition for metered buyers, which implies when payment parameters are needed. It does not explicitly name sibling alternatives or state when not to use this tool, but the context is clear enough for an agent to select it over the listed siblings.

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 clearly discloses the confirmation-before-delivery behavior ('nothing is delivered before that'), the founding-period free-lead incentive, and the prepaid-credit model. The sms_opt_in field carries the full legal consent text. Annotations (readOnlyHint=false, idempotentHint=false) are consistent with a registration creating a new record.

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 well-organized sentences: purpose first, then required inputs, then the confirmation behavior, then commercial terms. No wasted words; front-loaded and 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?

No output schema is provided and the description never mentions what the tool returns (e.g., a registration ID, status, or confirmation reference). Given the multi-step flow (register → email confirmation → verify_token from POST /v1/verify/check), the agent is left uncertain about the immediate response and error conditions.

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 67% (8 of 12 parameters described). Descriptions for npn, rail, states, and sms_opt_in are rich and add guidance (where to find NPN, default behavior, full consent text). However, agency_name, website, webhook_url, and contact_email have no descriptions, and the conditional requirement of webhook_url when rail='webhook' is not stated.

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 ('register'), a specific resource ('an insurance agency as a buyer'), and states the expected outcome ('receive consented consumer requests in its states'). The purpose is self-evident against siblings like agency_status, check_eligibility, and get_quotes.

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 implicitly distinguishes this from siblings (registration vs. status checks vs. quotes) and mentions 'Read /join for the terms' as a prerequisite. However, it never explicitly states when to use this tool versus alternatives (e.g., when to call check_eligibility first, or how it relates to request_agent_contact).

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 convey openWorld=true, non-idempotent, non-read-only, and non-destructive. The description adds significant behavioral disclosure: consent gating, scope-dependent sharing with up to 4 agents, optional marketing-partner sharing under sell_identity, receipting of deliveries/refusals, and a POST /forget revocation path. This is valuable context beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

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

The description is dense but efficient: purpose, prerequisites, scope behavior, consent rule, and revocation are all covered in three tight sentences. There is no filler or repetition of schema content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the absence of an output schema and the tool's sensitive consent and sharing behavior, the description does well by explaining prerequisites, scope effects, and revocation. It does not describe the response/return shape or how the consent URL is obtained, which leaves a small but notable 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?

With only 44% schema coverage, the description compensates for the most important parameters: quote_id, full_name, phone_number, and the consent object, including how consent can be granted. However, it does not explain optional parameters like best_time, email_address, street_address, preferred_channel, or verify_token, so the compensation is incomplete for a 9-parameter tool.

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, and clarifies the agents can 'quote firm and bind.' It also ties the tool to the quote_id prerequisite from get_quotes, distinguishing it from sibling tools like check_eligibility 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 clearly states preconditions: quote_id must come from get_quotes, and explicit consumer consent is required. It also explains how scope changes behavior. However, it does not name alternative tools or explicitly state when not to use this tool, so it stops short of full when/when-not guidance.

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