Georgiaautoquotes

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds value by enumerating exactly what data the read surfaces and by noting the required credential for access, which is useful behavioral context beyond the schema.

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 concise sentences with zero filler. The returned data list is front-loaded, and the prerequisite is stated in a single closing sentence. Every element 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 single-parameter, read-only status tool with no output schema and rich annotations, the description adequately conveys the purpose, the required input, and the scope of returned data. It loses one point because domain terms like 'states' and 'rail' are not elaborated, which could leave an agent unsure of their meaning without additional context.

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 sole parameter buyer_key is already documented as 'Issued to the licensed contact at confirmation'. The description repeats essentially the same information rather than adding detail like format, length, or how to obtain it. Baseline 3 is appropriate since the schema carries the semantic weight.

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 clear resource ('a registered agency') and a specific verb implied by 'status' — returns a defined list of fields: states, rail, terms, founding period, queued/delivered counts, credit balance, and licence verification. This distinguishes it from sibling tools like 'register_agency' or 'check_eligibility', though it does not name them explicitly.

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 scopes usage to 'a registered agency' and states a hard prerequisite — the buyer key issued at confirmation. This gives an agent a clear condition for when the tool applies, distinguishing it from registration or eligibility flows. It lacks explicit exclusion guidance (e.g., 'not for unregistered agencies'), but the context is clear enough.

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 establish read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds meaningful context beyond that: it must run before collecting personal details, and it returns licensed states, per-state capabilities, and agent counts. There is no contradiction with the annotations, and the privacy-aware timing requirement is a useful behavioral disclosure.

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

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

Three short sentences each add distinct information: purpose, ordering, and returned data. The imperative 'Call this first' is front-loaded enough to be noticed, and there is no fluff or repetition of the title or schema. This is an appropriately sized description for a simple eligibility check tool.

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 tool with strong annotations, the description covers purpose, timing, and response contents, which offsets the lack of an output schema. The phrase 'what we can do in each' is somewhat vague, and not-licensed or error cases aren't mentioned, but nothing critical is missing for basic invocation.

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 has a description; product does not), so the description should compensate. It does indicate that state filters results and can be omitted to see all licensed states, but it never explains the `product` parameter, its default/required status, or the two-letter state-code format. That is only partial compensation for the schema gap.

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 ('Check whether we can return quotes for a state') and adds a clear workflow position ('Call this first'). It also summarizes the return value, making the tool's role obvious. It doesn't explicitly differentiate from sibling tools, so it stops short of a 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?

'Call this first' and 'before any personal details are collected' give explicit when-to-use guidance tied to workflow ordering. There are no named alternatives or exclusions, but the ordering instruction is strong enough to guide tool selection. With no sibling list provided, this is appropriately 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?

Annotations already cover the safety profile with readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond that by disclosing that the tool is machine-readable, meant for pre-exchange evaluation, and enumerating the data-handling categories it covers. No contradiction with annotations; only the exact output format remains unspecified.

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 first front-loads the topic and the coverage list, and the second adds the machine-readability trait. Every sentence earns its place and supports tool selection.

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, zero-parameter metadata tool with annotations covering safety, the description is nearly complete: it states the content, the trigger condition, and the consumption model. The only gap is that 'machine readable' does not specify the return format, and no output schema exists to fill that in.

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 takes zero parameters, so the schema trivially covers all inputs; the description appropriately focuses on output content rather than inputs. Per the rubric, a zero-parameter tool earns a baseline of 4. There is nothing more the description could meaningfully add about 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 description clearly identifies the resource ('data use terms and consent wording') and enumerates its contents: who receives data, for what purpose, retention, revocation, and consent wording. It also distinguishes itself from siblings by tying to the request_agent_contact flow. However, it lacks an explicit retrieval verb, reading more as a statement of content than an action.

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 an explicit trigger: 'the exact consent wording to present before request_agent_contact,' telling the agent when to consult this tool. It also frames the tool as a pre-exchange evaluation step ('so you can evaluate the exchange before making it'), implying a before/after ordering with request_agent_contact. It does not explicitly rule out alternative tools, 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?

Annotation Contradiction: the annotations mark readOnlyHint as true, implying no persistent side effects, but the description says 'the request is still registered and a quote_id returned', which implies the server stores a new quote request. This is a write-like behavior, and idempotentHint=false further suggests repeated calls could create new registrations. The contradiction forces the lowest score.

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, each earning its place: the core action, the PII boundary, the unlicensed fallback, and the missing-facts behavior. Key information is front-loaded 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?

Given 10 parameters, no output schema, and only one described property, the description covers the main return behavior (prices when licensed, quote_id when not) and the server asking for missing facts. However, it omits the exact response shape, optional field semantics, and any rate-limit or licensing detail an agent might need in edge cases.

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 20% (only state is described), the description carries most of the parameter burden. It usefully clarifies that no PII is needed and all inputs are rating facts, but it does not explain the optional rating fields, their formats, or when they are required beyond the required list.

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 action ('Return indicative auto insurance prices') and a resource ('from multiple carriers'), with a clear qualifier about licensing. The purpose is immediately distinguishable from the sibling tools, which are about eligibility, market data, or 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?

Provides clear context on when to use this tool: when only rating facts are available and no PII like name, email, or SSN should be collected. It also explains the fallback behavior for unlicensed scenarios, but it does not explicitly name sibling tools or state when an alternative should be used instead.

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 mark the tool as read-only and non-destructive, and the description adds important behavioral details: absence of PII, pricing per record with a minimum, required payment via multiple mechanisms, and the 402-with-price-and-preview-count behavior when no payment is provided. It also points to /data.json for a sample and schema, which is genuinely useful.

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, with every clause contributing either dataset semantics, privacy guarantees, filtering and pagination, pricing and payment, or error behavior. It is front-loaded with the dataset identity and then builds outward into invocation details.

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?

There is no output schema, so the description carries the burden of explaining what the caller gets; it lists the record fields, the de-identification guarantees, filters, pagination, pricing, payment methods, failure behavior, and where to find sample data and schema. The description is complete for a paid read-only dataset tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With schema description coverage at only 36%, the description compensates by explaining the role of the filter parameters (state, since, until, door, vendor, limit, offset) and the payment parameters (prepaid buyer key, x402, MPP, AP2). It does not provide exact formats for date ranges or valid values for door/vendor, but it adds meaning well beyond the bare input 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 the resource as a purchasable, de-identified dataset of auto insurance demand records and names the exact fields it contains. It is specific enough to distinguish this tool from siblings like get_quotes, check_eligibility, or agency_status even though it does not explicitly name alternatives.

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 strong context: this is the tool for buying historical, de-identified demand records, with filters, pagination, and payment requirements. It does not explicitly state when not to use it or compare it with sibling tools, but the context is clear enough for an agent to recognize the intended 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 only mark readOnlyHint=false and destructiveHint=false, so the description carries the burden of explaining behavior. It does well by disclosing that metered buyers get a 402 with accepts[] until payment, and that records are projected to the consumer's granted scope. It stops short of detailing whether draining permanently removes records from the queue or other side effects, but the added context is meaningful and non-contradictory.

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 with no filler. The core action is front-loaded, the buyer key requirement is immediate, and the payment and scope caveats each earn their place. The structure is easy to scan and parse.

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 and moderate complexity, the description covers the key operational aspects: who can use it (buyer key), what happens for metered buyers, and the security boundary (scope projection). It does not describe the response format, pagination, or behavior when the queue is empty, but these are secondary given the tool's straightforward purpose.

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%, which already documents most parameters (mandate, payment, credential). The description adds value by explicitly stating that a buyer_key is required and by explaining why payment may be needed (x402 requirement, status 402, accepts[]). This goes beyond the schema and helps the agent understand the interplay between parameters, though buyer_key itself still lacks a schema 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 opens with a specific verb and resource: 'Drain the queue of pool records routed to you.' This clearly identifies the operation and scope, and the title 'Pull routed records (buyers)' reinforces the buyer-specific context. It is easy to distinguish from siblings like market_data or get_quotes even without opening schemas.

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 usage context is implied: this is for buyers pulling records routed to them, and it requires a buyer key. However, there is no explicit guidance on when to use this tool versus alternatives, nor any 'when not to use' or mention of sibling tools. The payment requirement hints at prerequisites but does not clarify when a mandate or credential is needed.

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 meaningfully extends beyond the annotations by disclosing the confirmation workflow: the licensed contact must confirm via an emailed link, and nothing is delivered before that. It also mentions the free founding period and prepaid credits, adding cost and timing context. The annotations do not contradict the description.

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 longer than average but every sentence carries useful information: target user, purpose, required inputs, confirmation behavior, and commercial terms. It is front-loaded with the core purpose and does not contain filler or repetition.

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

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

For a registration tool with 11 parameters and no output schema, the description gives a strong operational picture: prerequisites, required inputs, confirmation flow, delivery gating, and pricing. It omits only minor optional details such as website and verify_token, which the schema partially documents.

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 adds useful meaning by explaining the roles of key fields: agency name, NPN and state, appointment states, licensed contact details, and lead delivery method. It maps well to the required parameters and clarifies the purpose of contact_email. It does not explain optional parameters like website or verify_token in depth, but the schema partially covers those.

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 specific action ('register the agency'), the resource ('an insurance agency as a buyer'), and the outcome ('to receive consented consumer requests in its states'). It is distinct from sibling tools like agency_status or get_quotes because it focuses on registration rather than checking status or obtaining 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 provides clear context: it is for an AI representing a licensed insurance agency that wants to register to receive leads. It explains what information is needed and the intended workflow. It does not explicitly contrast with sibling tools or state when not to use it, 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 discloses significant behavioral context beyond the annotations: consent is mandatory before any data leaves, up to 4 agents are contacted under contact_consumer, sell_identity enables sharing with marketing partners, deliveries and refusals are receipted, and POST /forget revokes consent. This is rich, useful transparency for a side-effectful operation.

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

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

The description is three dense sentences with no filler: it front-loads the action, then covers prerequisites, scope semantics, and side effects. Every sentence contributes meaningful 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 9-parameter tool with a nested consent object and no output schema, the description explains prerequisites, consent flow, scope limits, receipting, and revocation. The main gap is that it does not describe the response format or error conditions, so the agent must infer what the tool returns.

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 only 44%, so the description compensates by explaining the core required parameters: quote_id, full name, phone, and especially the consent object's consent modes and the consent URL. It also explains the meaning of the scope parameter. However, optional parameters like best_time, preferred_channel, and verify_token are not addressed 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?

States a specific action—connecting the consumer with licensed agents who can quote firm and bind—and names the required prerequisite quote_id from get_quotes. This clearly distinguishes it from sibling tools like get_quotes, check_eligibility, and 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?

Provides explicit prerequisites: quote_id, consumer name and phone, and explicit consent. It also describes scope-dependent behavior for contact_consumer versus sell_identity, giving the agent clear context for when to use the tool. It does not explicitly contrast with sibling tools, but the conditions are unambiguous.

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