Texas Insurance

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

Annotations already communicate read-only, idempotent, non-destructive behavior, and the description adds useful context beyond that: the credential requirement and the exact fields the caller should expect. It does not specify error cases or formatting, but the annotations lower the burden and the added field list is valuable.

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 two compact sentences with no filler; the resource and contents are front-loaded and the required credential follows naturally. 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?

For a simple one-parameter read-only status tool with no output schema, the description covers the returned data and the required input. It does not explain error behavior or define domain terms like 'states' and 'rail,' but none of these are essential for selecting and invoking the tool correctly.

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

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

Schema description coverage is 100% and the buyer_key schema description already states that it is issued to the licensed contact at confirmation. The tool description repeats this requirement without adding format, length, or example details, so it adds no net semantic value 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 enumerates exactly what the tool reports (states, rail, terms, founding period, counts, credit balance, licence verification) for a registered agency, making the resource and scope clear. It lacks an explicit verb such as 'retrieves' and does not contrast with 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?

It gives clear context that the tool applies to a registered agency and requires the buyer key issued at confirmation, which implies when it is appropriate to call. It does not explicitly name alternatives such as check_eligibility or register_agency, nor state when not to use this tool, so guidance is implied rather than explicit.

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

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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context by specifying exactly what the tool returns—licensed states, per-state capabilities, and licensed agent counts—without contradicting any annotation.

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 deliver the tool's purpose, the call order, and the return contents with no wasted words. The critical instruction 'Call this first' is front-loaded, making the description easy to scan and act on.

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?

Despite having no output schema, the description explains the response shape (licensed states, actions available, agent counts) and the correct usage context. For a simple eligibility-checking tool with read-only annotations, this is sufficient for an agent 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 describes the state parameter, and product is self-explanatory with a single enum value and default. The description reinforces the state-centric behavior but does not clarify the optionality of state or the product parameter's role, leaving some burden unmet at 50% schema coverage.

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

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

The description uses a specific verb and resource: it checks whether quotes can be returned for a state before collecting personal details. It also identifies the tool's distinct output—licensed states, capabilities per state, and agent counts—clearly separating it from quote retrieval and contact-request siblings.

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

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

The imperative 'Call this first' gives explicit sequencing guidance, and 'before any personal details are collected' clarifies the intended context. It does not explicitly name when not to use the tool or point to an alternative, so it falls just short of full alternative-routing guidance.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering safety. The description adds value by stating the output is machine-readable and intended for evaluating an exchange before proceeding, which gives useful behavioral context for a zero-parameter read-only tool.

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

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

Two sentences, both information-dense: the first enumerates all the policy dimensions covered, and the second states machine readability and the evaluation use. No filler or repetition, with the most important details 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 complete: it tells the agent what data the tool returns, why it matters, and when to call it. The exact machine-readable format is unspecified, but that is not needed for correct selection and 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?

The tool has zero parameters, so schema coverage is 100% by vacuity; there are no arguments for the description to explain. The baseline for zero-parameter tools is 4, and the description appropriately focuses on output content rather than 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 states precisely what the tool provides: machine-readable data-use terms covering recipients, purpose, retention, revocation, and exact consent wording to use before request_agent_contact. This goes well beyond the title and clearly distinguishes it from siblings like market_data 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 explicitly positions this as the step before request_agent_contact: the consent wording is for presenting before that call, and the machine-readable output is for evaluating the exchange first. It doesn't discuss alternatives or when-not-to-use, but the intended 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?

Beyond the readOnlyHint and destructiveHint annotations, the description discloses valuable behavior: no PII is required, unlicensed requests still produce a quote_id for licensed agents, and the server asks only for missing facts. These details meaningfully shape agent expectations. 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?

Three concise sentence, each adding necessary context: the main behavior, the PII limitation, and the missing-facts behavior. It is front-loaded with the core purpose and contains no filler.

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 quote tool with no output schema, the description covers the key operational context: licensing, required input type, and the fallback registration behavior. It could be slightly richer about the exact response shape beyond the quote_id, but the remaining ambiguity is minor.

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 should compensate. It does add useful high-level meaning by explaining that only rating facts are accepted and PII must be excluded. However, it does not elaborate on ambiguous parameters like prior_continuous or the coverage enum values, leaving some semantic gaps.

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: it returns indicative auto insurance prices from multiple carriers. It also clarifies the licensing condition, which distinguishes this from related tools like check_eligibility or request_agent_contact. The purpose is unmistakable.

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

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

The description clearly establishes when this tool is appropriate: when indicative pricing is needed and only rating facts are available. It also explains the unlicensed-carrier fallback behavior, which helps an agent decide what to do with the result. It does not explicitly name sibling alternatives, but the context is strong 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 declare readOnlyHint=true and destructiveHint=false, so the safe-read nature is already known. The description adds meaningful behavioral detail: the data is de-identified and never contains PII, payment is required before records are returned, failure returns a 402 with price and preview count, and there is a per-record price with a minimum per pull. It does not explicitly state pagination limits or rate limits, but the schema's maximum on limit covers the main cap. It adds material context beyond 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 compact for the amount of information it carries: one dense sentence listing the dataset contents and exclusions, then short actionable sentences for filters, pricing, payment, and the sample endpoint. It front-loads the dataset identity and PII exclusions before the mechanics. It is slightly list-heavy, but 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?

For a paid data-pull tool with 11 parameters, no output schema, and only 36% schema coverage, this description covers the important gaps: what the data is, what it excludes, how to filter, how to pay, what failure looks like, and where to find the schema and sample. It doesn't describe the exact response format beyond the preview count, but the pointer to /data.json mitigates that. It is strong given the available structured metadata.

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 clarify parameters that the schema leaves bare. It does: it explains door and vendor as the 'door' and 'AI vendor that asked,' and states the filters (state, since, until) and pagination (limit, offset). It also explains payment parameters (buyer_key, x402 payment, MPP credential, AP2 mandate) at a conceptual level. It doesn't give exact formats for the date-time strings or the payment payload, but it goes well beyond the sparse 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 title states exactly what the tool provides ('Buy de-identified demand records') and the description gives a specific inventory of the dataset plus what is excluded. It is clearly distinct from siblings like get_quotes or pull_requests because it names the resource (de-identified demand dataset) and the sales/purchase action 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 explicitly lists the filter dimensions (state, since, until, door, vendor), the pagination controls (limit, offset), and the payment paths (prepaid buyer key, x402, MPP, AP2). It also tells the agent what happens without payment (402 with price and preview count), which is a strong when-to-use and how-to-use signal. It doesn't name a sibling alternative, but the resource is so specific and the payment guidance is so actionable that an agent can select it confidently.

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 details beyond the all-false annotations: the queue-draining effect, the x402 payment gate with status 402 and accepts[], and the scope-narrowing guarantee. This gives an agent a realistic model of what will happen and what constraints apply.

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

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

The description is three sentences with no fluff: it leads with the core action, states the prerequisite, then explains conditional payment and data-scope behavior. 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?

The description covers the main invocation contract: who can call it, what queue it affects, how metered payment works, and how scoping is constrained. It does not describe the success response shape or empty-queue behavior, but with no output schema and a clear action, it is still enough for an agent to 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?

The input schema covers three of four parameters, and the description adds contextual meaning by tying buyer_key to the required buyer identity and payment to the metered-buyer payment flow. The mandate and credential parameters are already explained in the schema, so the description does not need to repeat their syntax.

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 action and resource: 'Drain the queue of pool records routed to you.' It also establishes the buyer-specific context, which separates it from siblings like get_quotes or market_data. The title reinforces the meaning, so an agent can tell what this tool does at a glance.

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 should be used when a buyer wants to pull routed pool records, and it explicitly states the prerequisite of a buyer key. It also explains the payment requirement for metered buyers. However, it does not explicitly state when not to use it or name 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 only carry readOnly=false and idempotent=false, so the description carries the behavioral burden and delivers: the email-confirmation gate, that nothing is delivered before confirmation, the founding-period free leads, and prepaid-credit billing. These are exactly the side effects an agent needs to set expectations before invoking a mutating registration flow.

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: audience and purpose, required data, confirmation workflow, and commercial terms. The core scoping is front-loaded and there is zero filler.

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, multi-step registration flow with no output schema, it covers prerequisites, required data, the confirmation gate, delivery rails, billing, and points to /join for terms. It does not describe the response shape or the post-confirmation next step (e.g., checking agency_status), but the sibling list partially 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?

Schema coverage is 64%, putting the baseline at 3, but the description adds role-level meaning: the contact is the 'licensed contact' who confirms by email, states are where the agency is 'appointed', and rail is 'how leads should arrive'. It also adds the 'US mobile' constraint. However, website and webhook_url get no semantic anywhere, leaving a genuine 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?

States a specific verb (register), a resource (an insurance agency as a buyer), and the precise outcome (receive consented consumer requests in its states). The audience qualifier 'For an AI working for a licensed insurance agency' and the contrast with siblings like get_quotes, market_data, and agency_status 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?

Gives clear context: use this tool when a licensed agency needs to register to receive consented requests, and names the prerequisites (NPN, appointments, licensed contact). It does not explicitly name alternatives or exclusions (e.g., 'if already registered, use agency_status'), so some routing between siblings is left to inference.

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 substantial behavioral context beyond the annotations: it discloses that up to 4 licensed agents receive the request under contact_consumer scope, that sell_identity also permits sharing with marketing partners, that nothing leaves without consent, and that every delivery and refusal is receipted with POST /forget available for revocation. This gives the agent a realistic model of the side effects and privacy boundaries.

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 every sentence earns its place: purpose, prerequisites, consent mechanics, scope behavior, privacy guarantee, and revocation. It is front-loaded with the core action and keeps the more nuanced consent details organized near the end 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?

For a 9-parameter tool with nested objects and no output schema, the description covers the essential behavioral contract well: what is sent, to whom, under what consent, and how to revoke. It could be more complete by describing the response shape or the elicitation flow's return value beyond mentioning the consent URL, but the core invocation context is sufficiently specified.

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 description coverage, the description compensates for the most important parameters: quote_id, full_name, phone_number, and the consent object with its scope semantics. It explains how consent is granted, what the two scopes mean, and how revocation works. However, optional parameters like best_time, email_address, preferred_channel, and full_name are not elaborated 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 opens with a specific verb and resource: connecting the consumer with licensed insurance agents who can quote firm and bind. It clearly distinguishes this tool from siblings like get_quotes, register_agency, and check_eligibility by focusing on the contact request action. The consent requirement is also stated up front, making the tool's role 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 explicitly names get_quotes as the source of the required quote_id and insists on explicit consumer consent, which is the key precondition for using the tool. It does not explicitly list excluded scenarios or compare against alternative contact methods, but the prerequisites and scope conditions are clear enough to guide appropriate invocation.

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