Nevada Insurance

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

Annotations already disclose readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the output scope (a status snapshot of seven statistics) but repeats the buyer-key auth requirement already present in the parameter schema, and discloses no side effects, rate limits, or other novel behaviors.

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 zero filler. The high-value field enumeration is front-loaded, and the access requirement is stated once at the end. 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?

With one fully documented required parameter and no output schema, the description's enumeration of returned fields gives an agent enough to understand what the tool provides. It is complete for a simple read-only status lookup, though domain terms (states, rail, terms) and value formats are left unexplained.

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 single buyer_key parameter is already documented as 'Issued to the licensed contact at confirmation'. The description merely restates this ('Requires the buyer key issued at confirmation') without adding format, acquisition method, or usage nuance, so the baseline 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 opens with 'For a registered agency' and enumerates the exact data returned (states, rail, terms, founding period, queued/delivered counts, credit balance, licence verification), making the resource and scope very specific and distinct from the sibling check_eligibility. However, it lacks an explicit verb such as 'gets' or 'returns', and does not directly differentiate itself from its sibling, so it stops short of 5.

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 for when the tool applies ('For a registered agency') and states the prerequisite (buyer key issued at confirmation). It does not, however, mention alternatives, exclusions, or when not to use this tool relative to check_eligibility, so it is strong on context but not on 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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the description doesn't need to restate safety. It adds useful return-value context (licensed states, capabilities, agent counts) but does not disclose behaviors beyond that. 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 zero filler. The action and 'call this first' directive lead, followed by a compact summary of return contents. Every sentence contributes.

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, ordering, and broad return contents, but leaves the effect of the optional state parameter ambiguous (does it filter to one state or return all?). The product parameter is also unexplained outside the schema. For a simple read-only tool, these are moderate 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 50%: state is documented but product is not. The tool description mentions 'state' but doesn't explain how the optional state filters results or what the product parameter means. It adds little semantic value beyond the schema, and with low coverage the description should compensate but doesn't.

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') with a clear resource ('whether we can return quotes for a state') and a timing qualifier ('before any personal details are collected'). It distinguishes itself from get_quotes by being a pre-check and outlines the output content (licensed states, capabilities, 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?

'Call this first' is an explicit ordering instruction that tells the agent when to invoke this tool. It doesn't name alternative tools or explain when not to use it, so it falls short of full routing guidance, but the first-step directive is clear and actionable.

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 value beyond annotations by disclosing that the return is machine-readable and by describing the content categories the agent will receive (retention, revocation, consent text). 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 carry substantial information with no filler: the first enumerates the five content aspects, the second adds the machine-readable affordance and its purpose. Slightly dense as a run-on opening sentence, but every clause earns its place and the key question 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?

For a zero-parameter, read-only tool with no output schema, the description is largely complete: it states what the tool returns, why it is machine-readable, and when to fetch it (before request_agent_contact). Minor gap: it does not describe the exact output format or field structure, but the content enumeration largely compensates.

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 schema coverage is 100%, so the baseline is 4. The description compensates by explaining what the returned artifact contains (data handling terms, consent wording), which is the only semantic information an agent needs for a parameterless lookup.

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 concrete question ('What happens to anything you send us') and enumerates exactly what the tool provides: recipient, purpose, retention, revocation, and consent wording. This clearly identifies the resource (data use/consent terms) and distinguishes it from siblings like request_agent_contact, agency_status, or 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 gives explicit timing guidance: the consent wording should be presented 'before request_agent_contact', and the data is for evaluating 'the exchange before making it'. It names the relevant sibling and anchors usage to that workflow. However, it does not state when to avoid this tool or name exclusion cases, so it falls just 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?

Annotation Contradiction: readOnlyHint=true indicates the tool does not modify state, yet the description says 'the request is still registered and a quote_id returned' even when unlicensed. This implies a persisted quote/request record, which is a state-changing side effect. The description directly contradicts the annotation, so the behavioral transparency score must be 1.

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 sentences cover the core outcome, input constraints, licensing fallback, and missing-facts behavior without excessive filler. It is appropriately sized for the tool's complexity and front-loads the primary purpose before edge conditions.

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, no output schema, and only 20% schema description coverage, the description leaves major gaps: return structure beyond 'prices' or 'quote_id', validation behavior, optional parameter details, and licensing conditions. The licensing fallback is helpful, but the definition is not complete enough for an agent to call this robustly.

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 the other 8 parameters. It only broadly says 'takes rating facts only' and lists what is not required, but it does not explain annual_mileage, violations_3yr, years_licensed, prior_continuous, or vehicle_make_model semantics beyond their names. The missing-facts sentence hints the server will ask, but it does not document parameter meaning.

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 names a specific verb and resource: 'Return indicative auto insurance prices from multiple carriers.' It also explains the licensing-dependent behavior and the no-PII input rule, which clearly separates this from sibling tools like request_agent_contact or check_eligibility. The purpose is unambiguous and agent-actionable.

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 takes rating facts only, requires no personal identifiers, and explains what happens when the entity is not licensed to display prices. It does not explicitly name alternative tools or state when not to use it, but the context is specific enough for an agent to know when this tool applies.

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 goes far beyond the annotations by disclosing de-identification protections, the exact fields included and excluded, payment methods, the 402-with-preview behavior without payment, and the availability of a sample schema. This is rich behavioral context that complements the readOnly/non-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 description is dense but not bloated: each sentence contributes dataset scope, filters, pricing/payment, error behavior, or schema access. It could be easier to scan as structured bullets, but it is appropriately sized for the tool's complexity.

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, payment-gated tool with no output schema, the description covers the data content, de-identification guarantees, filters, pricing, payment modes, the no-payment 402 case, and a link to the full schema. It does not spell out exact output formatting or every parameter constraint, but the /data.json pointer and the level of detail make it sufficiently complete.

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 description coverage, the description carries most of the parameter meaning. It explains filter dimensions (state, since, until, door, vendor), pagination (limit, offset), and maps payment concepts to the relevant params (x402, MPP, AP2, buyer key). It does not fully specify value formats or how payment params are transported, but it adds substantial meaning beyond the bare 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 clearly identifies a specific resource—the de-identified demand dataset—and a concrete operation: retrieve/buy records with filtering and pagination. It enumerates the record fields and explicitly distinguishes this dataset from other possible tools by saying it covers every auto insurance request made here.

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 for when to use the tool: to obtain demand records, with state/date/door/vendor filters, pagination, and payment requirements. It does not explicitly name sibling alternatives or say when not to use it, but the context is unambiguous enough for an agent to route to this tool for dataset access.

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 annotations, the description discloses several concrete behaviors: the queue-draining effect, the x402 payment requirement with status 402 and accepts[], and the security guarantee that records are projected only to the granted scope. This adds meaningful context about side effects, authentication, and authorization that the boolean annotations do not convey.

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

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

Three sentences deliver purpose, prerequisites, payment behavior, and security scope with no filler. Each sentence earns its place, and the most important action 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?

The description covers the core action, required key, payment edge case, and data scoping. Since there is no output schema, one might want a bit more about the exact return shape or what happens after the queue is drained, but "drain the queue" strongly implies the outcome. Overall it is sufficiently complete for a capable 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 75%, and the schema already documents mandate, payment, and credential. The description fills the gap for buyer_key by stating it is required, and it explains the role of payment in resolving the 402 status. This adds meaningful semantic context beyond the bare schema fields.

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." This clearly distinguishes the tool from sibling tools like agency_status or get_quotes, which serve different functions. The title reinforces the buyer-facing scope.

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 clearly: "Requires your buyer key." It also gives conditional usage context by explaining that metered buyers need to supply x402 payment until the 402 requirement is satisfied. It does not explicitly name alternatives or when not to use the tool, but the context is clear.

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

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

The description discloses that the licensed contact must confirm via an emailed link and that no leads are delivered before confirmation. It also mentions the founding-period/free-credit arrangement and points to /join for terms. Annotations already indicate a non-read-only, non-destructive write operation, and the description adds meaningful workflow context without contradicting 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?

Four sentences front-load the purpose and prerequisites, then cover confirmation, delivery gating, and commercial terms. It is dense but not padded; every sentence adds decision-relevant 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 registration flow with 11 parameters and no output schema, the description covers who should invoke it, what inputs are needed, how confirmation works, and the commercial model. It doesn't describe the response shape or post-registration state, but the decision and invocation path are clear.

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?

It translates the main schema fields into natural-language requirements: agency name, NPN and state, appointed states, contact identity, and lead delivery mode (mapping to the rail enum). With schema coverage at 64%, this materially helps; however, optional fields such as website and verify_token are not mentioned in the description.

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 names a specific action ('register the agency') and a precise outcome ('receive consented consumer requests in its states'), which clearly sets it apart from sibling tools like request_agent_contact or get_quotes. The title and description align, so an agent can tell what this operation does without opening the schema.

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 opens with the intended actor and scenario ('For an AI working for a licensed insurance agency') and states the purpose: registering the agency to receive consented requests. It does not explicitly name alternative tools or give 'when not to use' guidance, 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 goes well beyond the annotations by explaining exactly what happens: up to 4 agents receive the request, sell_identity expands sharing to marketing partners, nothing leaves without consent, deliveries/refusals are receipted, and POST /forget revokes. It also clarifies the consent-granting modes. No contradiction with annotations 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 dense but front-loaded with the core purpose, then layers prerequisite and behavioral detail. It is slightly run-on and uses jargon like 'quote firm and bind' and 'elicitation,' but each sentence adds meaningful guidance, so the length is justified.

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 9 parameters, nested consent objects, and no output schema, the description covers purpose, prerequisites, consent mechanics, scope effects, and revocation. It does not explicitly describe the response shape or return values beyond saying deliveries are receipted, which is a notable gap given the missing 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?

With schema description coverage at only 44%, the description compensates well for the most important parameters: quote_id, full_name, phone_number, and the nested consent object, including scope semantics. However, optional parameters such as best_time, preferred_channel, email_address, and verify_token receive no meaning beyond 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 a specific action — connecting the consumer with licensed insurance agents — and the resource it acts on. It clearly differentiates the tool by tying it to a prerequisite (quote_id from get_quotes) and by explaining scope-driven behavior, which distinguishes it from siblings like get_quotes or register_agency.

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 prerequisites and context: quote_id, consumer name/phone, explicit consent, and scope. It does not explicitly name when NOT to use the tool or point to alternatives, but the dependency on get_quotes and the consent condition make the intended usage fairly unambiguous.

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