Texasautoquotes

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds behavioral context by stating that the buyer key is required and was issued at confirmation, which is an important access prerequisite not fully captured by 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 a single efficient sentence that front-loads the resource and returned fields, then states the key requirement. There is no filler or redundant restatement of the tool name.

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 status endpoint with one parameter and rich annotations, the description covers the essential information: what data is returned and what credential is needed. It does not describe error behavior or edge cases, but those are not critical for this tool's simple scope.

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%, so the buyer_key parameter is already documented. The description repeats the authentication requirement without adding meaningful new detail beyond the schema, meriting the baseline score for well-covered 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 enumerates the resource (a registered agency) and the specific status fields returned, such as rail, terms, credit balance, and verification. It stops short of an explicit verb like 'retrieves' or 'returns', and does not directly contrast with sibling tools, though the title and content make the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description establishes clear usage context: it applies to a registered agency and requires the buyer key issued at confirmation. It does not explicitly state when to prefer this over sibling tools, but the prerequisites and data scope give an agent enough context to use it appropriately.

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 safety profile is covered. The description adds useful context beyond annotations: it requires no personal details and summarizes the return payload (licensed states, capabilities, agent counts), which helps an agent anticipate behavior.

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 concise sentences: purpose, placement in the workflow, and return value. There is no repetition of schema information or filler, and the most important guidance ('Call this first') appears early.

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 low-complexity, read-only pre-check with no output schema, the description covers invocation timing, privacy condition, and a high-level return summary. It could be more precise about behavior when state is omitted or how the returned data is structured, but it gives an agent enough to invoke it 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?

The schema already documents state as a two-letter code and product via enum/default. The description adds no parameter-level meaning beyond mentioning 'a state', and it leaves ambiguous whether state is optional and how omission affects results. With 50% schema coverage, this is adequate but does not fully compensate.

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 we can return quotes for a state'), and positions it as a pre-personal-details step. It clearly differentiates from siblings like get_quotes by framing itself as the first call in a workflow.

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 explicitly says 'Call this first' and specifies the context (before personal details are collected). However, it does not name alternative sibling tools or state when not to use it, so it stops short of full when-to-use/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?

The description adds meaningful behavioral context beyond the annotations: it details the categories of information returned (recipients, purpose, retention, revocation, consent wording) and states the output is machine-readable. It does not contradict the readOnlyHint, idempotentHint, or destructiveHint 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 compact and front-loaded with the core purpose. Each phrase in the list adds a distinct piece of information, and the second sentence justifies the machine-readable format. There is minimal waste.

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 and no parameters, the description carries the burden of explaining what the agent will receive. It enumerates the content areas well and notes machine readability. It could be slightly more explicit about the exact return format, but for a zero-parameter read-only tool, this is adequate.

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, so the baseline is 4. The description further helps by explaining what kind of content the tool provides, which is more than necessary given the empty 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 explains what the tool provides: data use terms covering recipients, purpose, retention, revocation, and consent wording. It also names a sibling (request_agent_contact) to clarify its role, though it lacks an explicit action verb like 'retrieve' or 'return'.

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 to use the consent wording 'before request_agent_contact' and that the data is for evaluating the exchange before making it. This gives a clear when-to-use context, though it does not mention when not to use the tool or alternative tools.

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 and destructiveHint=false, but the description adds meaningful behavior beyond that: unlicensed registrations still return a quote_id, missing facts trigger a targeted server request, and no PII is required. This is valuable non-contradictory context that helps an agent anticipate edge cases.

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 dense sentences, each earning its place: the first states the core purpose, the second sets the privacy boundary, and the third explains fallback and interactive behavior. The main action is front-loaded and there is no filler or 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 must explain return behavior, and it does: it says prices are returned when licensed, and a quote_id is returned otherwise. It also describes the interaction when facts are missing. However, it does not detail the structure of the price response, possible error cases, or how the quote_id should be used, leaving minor gaps for an 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 only 20%, so the description carries a heavy burden. It compensates by framing all inputs as 'rating facts only' and explicitly listing what is not required (name, phone, email, SSN, licence number), which prevents parameter misuse. It does not explain individual fields like vehicle_make_model or annual_mileage formatting, so the compensation is partial.

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: 'Return indicative auto insurance prices from multiple carriers,' and adds a key scope condition, 'where this entity is licensed to show them.' It clearly distinguishes this tool from siblings like check_eligibility and request_agent_contact by focusing on price quotes rather than eligibility status or agent contact routing.

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 this tool: for indicative quotes using rating facts only, and it explains the fallback behavior when the entity is not licensed. However, it does not explicitly name alternative tools or state exclusion criteria, so the guidance stops short of full when-to-use versus when-not-to-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?

Beyond readOnlyHint=true, the description discloses PII exclusions, pricing, payment methods, and the 402 response with price/preview count when unpaid. It also points to a sample and schema endpoint. 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?

The description is information-dense with almost no filler and front-loads the dataset's purpose. The first sentence is a long comma-separated list, but each clause adds meaning; minor restructuring would make it even cleaner.

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, no-output-schema commercial data tool, this is unusually complete: it covers data fields, privacy guarantees, filters, pricing, payment options, failure behavior, and where to find the exact schema. An agent has enough to invoke it and understand the main 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 coverage is low (36%), but the description compensates by naming the filter parameters (state, since, until, door, vendor, limit, offset) and mapping payment methods to buyer_key, payment, credential, and mandate/AP2. It leaves value formats and enum-like options undefined, hence not a 5.

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-resource pair (buy the demand dataset) and enumerates exactly what records contain, which distinguishes it from siblings like get_quotes or pull_requests. The title reinforces the resource and the description adds concrete 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?

It gives clear context for when to call this tool: whenever de-identified auto-insurance demand records are needed, with filtering and payment. It does not explicitly name alternatives 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 adds meaningful behavioral context beyond the annotations: the buyer key requirement, the metered payment mechanism with status 402 and accepts[], and the privacy-oriented scope projection ('never wider'). The annotations only carry false hints, so the description carries the burden and fulfills it well. No contradiction with annotations was found.

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 compact sentences deliver the core action, prerequisites, payment behavior, and scope constraints. The most important information is front-loaded, and every sentence contributes distinct value. No filler or repetitive wording.

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 the essential invocation context: action, prerequisite, conditional payment, and data projection scope. It does not describe the shape of returned records or what 'draining' means for queue state in detail, but the description is sufficient for an agent to understand the operation and its conditions. Slight gap around return semantics keeps it from 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 75%, and the description adds value by clarifying that buyer_key is the caller's required key and explaining the role of the payment parameter in the x402 flow. This goes beyond the raw schema, which leaves buyer_key undescribed. The mandate and credential parameters are not elaborated further, but the existing schema descriptions cover them.

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—'Drain the queue of pool records routed to you'—with a clear resource and scope. The title 'Pull routed records (buyers)' reinforces the intended audience. It does not explicitly distinguish itself from sibling tools, but the verb and resource are specific enough that an agent can identify what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context for when to use the tool: to drain queued records routed to the caller. It also states a prerequisite ('Requires your buyer key') and a conditional requirement for metered buyers ('receive an x402 payment requirement until a payment is supplied'). It does not explicitly name alternatives or exclusions, but the conditions are 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 adds meaningful behavioral context beyond the annotations: confirmation by email link, nothing delivered before confirmation, and the founding-period/pricing model. It does not cover every side effect, but it discloses the most important workflow and safety-relevant behavior.

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 purpose, required information, confirmation behavior, and commercial terms without redundancy. The description is front-loaded and every sentence contributes useful information for a tool with 11 parameters.

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

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

For a complex registration tool with no output schema, the description provides enough context to call it correctly: who may use it, what data is needed, how delivery works, and what happens before leads are sent. Minor gaps remain, such as the exact response shape and the sms rail not mentioned in the description, but these are not blocking.

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 well by mapping required inputs in plain language: agency name, NPN, state, appointed states, licensed contact details, and lead delivery method. It does not explain website, webhook_url, or verify_token, but those are either optional or already covered by schema descriptions.

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

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

The description uses a specific verb and resource: register the agency to receive consented consumer requests in its states. It clearly distinguishes this tool from siblings like agency_status or check_eligibility by stating the registration outcome and target 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 gives clear context for when to use the tool: an AI working for a licensed insurance agency that wants to register as a buyer. It does not explicitly compare against sibling tools or state when not to use it, but the intended scenario is 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?

The annotations only say this is a mutating, non-idempotent side-effecting action; the description adds substantial color: up to 4 licensed agents receive the request under contact_consumer, sell_identity widens sharing to marketing partners, nothing leaves without consent, deliveries/refusals are receipted, and POST /forget revokes. This goes well beyond the structured fields.

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 text is dense but not bloated: it front-loads the core action, then prerequisites, scope effects, and revocation. Some clauses are packed with semicolons and em dashes, which makes parsing slightly harder, but 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 9-parameter tool with nested objects and no output schema, the description covers the consent flow and scope semantics well but omits return/shape information beyond 'receipted' and says nothing about verify_token's effect on price/contact priority. This leaves an agent short on how to fully leverage the request.

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%, and the description partially compensates by explaining the roles of quote_id, full_name, phone_number, and consent (including the consent URL and scope field). But it still leaves best_time, preferred_channel, email_address, and verify_token unaddressed in prose, so it doesn't fully fill the low-coverage 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 clear verb and resource: connect the consumer with licensed insurance agents who can quote firm and bind. It distinguishes itself from siblings by referencing the upstream get_quotes and the consent mechanism, so an agent knows this is the contact-brokering step, not quoting or eligibility.

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 gives explicit prerequisites: a quote_id from get_quotes, the consumer's name and phone, and explicit consent. It also explains how consent can be obtained. However, it does not name alternatives or state when not to use the 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.