Oklahoma Insurance

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 description didn't need to restate safety. The description adds useful behavioral context: it requires the buyer key issued at confirmation (an authorization credential), and it lists the specific data dimensions returned, which helps the agent understand the scope of the read operation. It could add a bit more about error conditions (e.g., invalid key, unregistered agency), but the annotations plus the detailed field list give strong transparency.

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 sentence that packs all essential information: the tool scope (registered agency), the exact data returned, and the credential requirement. There is no fluff or repetition of schema data. The information is front-loaded with the tool's purpose before the credential note.

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 tool with one parameter, a comprehensive output schema, and full annotations (read-only, idempotent, non-destructive), the description is nearly complete. It lists all returned data dimensions and the required credential. It doesn't describe output format or error behavior, but with no output schema and a clearly enumerated result list, an agent has enough to decide whether to call it and how to use the result. A minor gap is not mentioning what happens if the key is invalid, but this is not critical for selection.

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 schema already documents the buyer_key parameter as 'Issued to the licensed contact at confirmation.' The description reinforces this by stating the key is required and was 'issued at confirmation,' adding the nuance that this key is the mechanism for accessing the status. Since there is only one parameter and it is fully covered by the schema, the description provides adequate additional 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 begins with a clear verb-like phrase ('For a registered agency') and enumerates exactly what the tool returns: states, rail, terms, founding period, queued and delivered counts, credit balance, and licence verification status. This is much more specific than the title 'Agency status (buyers)' and clearly identifies the tool as a status/read query, distinguishing it from sibling actions like check_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?

The description states the tool is for a 'registered agency' and that it 'Requires the buyer key issued at confirmation.' This gives clear context on when to use it (when you have a registered agency and the buyer key) and signals that the buyer key is a prerequisite. It does not explicitly name alternatives or exclusions, but the parameter requirement and read-only nature provide enough guidance for an agent to select 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 read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond that: no personal details are needed, it should be called first, and it returns licensing status, allowed actions, and licensed agent counts. This goes beyond the safety profile captured by annotations.

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

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

Three short sentences, each earning its place: purpose, call ordering, and return contents. No filler or redundant restating of the tool name or schema.

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 eligibility check with annotations covering safety and no output schema, the description gives enough information for an agent to know when to call it and what to expect. The only minor gap is lack of detail about optional parameters, but this does not undermine usability.

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 50%, and the description does not add parameter-level detail beyond what the schema already provides. The product parameter is explained by its enum/default, and state has a schema description, so the description's omission is not critical, but it also does not clarify behavior when state is omitted.

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: checking eligibility for returning quotes, explicitly before personal details are collected. It also differentiates from likely siblings like get_quotes by positioning itself as the upfront availability check and specifying the returned data: states, capabilities, and 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 clear, direct guidance that establishes sequencing relative to later steps. It gives a clear context for use—before personal details are collected—but does not explicitly name alternative tools or conditions where another sibling should be chosen 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description is not burdened with basic safety disclosure. It adds valuable behavioral context: the tool returns machine-readable data-use terms covering recipients, purpose, retention, revocation, and consent wording, which is far more informative than the annotations alone.

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 sentences with no filler. It front-loads the core concept, enumerates the specific contents, and closes with the practical use case. Every clause earns its place and improves an agent's ability to decide when to call it.

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 informational tool, this description is complete. It specifies what the tool returns, why it matters, and when to invoke it relative to request_agent_contact. No output schema exists, but the description sufficiently conveys the tool's value and usage 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?

The tool has zero parameters and schema coverage is 100%, so there is nothing for the description to add about individual parameters. The baseline of 4 applies here, and the description provides no conflicting or misleading parameter information.

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 ('what happens to anything you send us') and a concrete resource (data use terms and consent wording), explicitly listing the dimensions covered: recipients, purpose, retention, revocation, and consent wording. It also distinguishes itself from siblings by tying itself to request_agent_contact, making its role clear.

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

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

The description clearly states when to use the tool: before request_agent_contact and before making an exchange, to evaluate data handling terms. It does not explicitly name alternatives or exclusion conditions, but the contextual guidance is sufficient for an agent to route correctly.

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 useful context about licensing, quote_id fallback, and missing-facts prompts, but it also says the request is 'still registered and a quote_id returned,' which implies persistence/side effects. This contradicts the readOnlyHint=true annotation, so per the rubric it must score 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?

Three compact sentences, front-loaded with the core purpose, followed only by behavior and constraints. 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 10-parameter tool with no output schema, the description covers the main return value (prices from carriers), the unlicensed fallback (quote_id), and the missing-facts interaction. It does not specify the exact response shape for licensed quotes, but it is otherwise sufficient.

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

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

With only 20% schema description coverage, the description compensates by framing inputs as 'rating facts only,' excluding PII, and noting the server will ask for missing facts. However, it does not explain individual fields such as prior_continuous or violations_3yr, leaving them to inference.

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 'Return[s] indicative auto insurance prices from multiple carriers' and scopes this to where the entity is licensed. This clearly differentiates it from siblings such as check_eligibility and market_data.

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

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

It clearly conveys when to use the tool (to get an indicative quote using rating facts only) and explicitly says no PII is required. It does not name alternatives or state when not to use it, so it falls 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?

Beyond the annotations, it discloses the de-identification guarantee, per-record pricing with a minimum per pull, accepted payment mechanisms, and the no-payment 402 response containing price and preview count. It also points to /data.json for the schema. Nothing here contradicts the readOnly or 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 every sentence adds a distinct facet: dataset contents, PII exclusions, filters, pricing, payment, and schema location. It could be split into structured bullets, but it is not padded.

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 tool with no output schema, the description covers data scope, filtering, pagination, payment, error behavior, and where to find a free sample and schema. The successful response shape is implied rather than specified, which prevents a 5.

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

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

Schema description coverage is only 36%, but the description compensates by explaining the filter set (state, since, until, door, vendor, limit, offset) and mapping payment parameters to buyer_key, x402, MPP, and AP2. It does not give exact formats for date or payload values, relying on the schema and /data.json.

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 supplies a specific verb and resource: Buy de-identified demand records (the dataset). The description enumerates exactly what the dataset contains, including fields, outcomes, and PII exclusions, 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 gives clear invocation context: filter the demand dataset by state, date, door, vendor, limit, and offset, and pay with one of the listed methods. It does not name alternative tools or state when not to use this tool, 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?

Beyond the annotations (readOnly=false, destructive=false), it discloses that the operation drains a queue, that metered buyers face an x402 payment requirement with accepts[] until payment, and that records are scope-projected to the consumer's grant. These are substantive behavioral details not present in the schema or annotations.

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

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

Three tight sentences: action, prerequisite/payment behavior, and scope safety. No filler, and the core action is front-loaded in the first sentence.

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, auth prerequisite, payment gating, and scope projection, but there is no output schema and the description does not explain what a successful call returns or what happens when the queue is empty. For a drain operation, return/empty-queue behavior would be useful.

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 schema already describes mandate, payment, and credential. The description adds meaning to buyer_key ('your buyer key') and explains the payment-related flow ('Metered buyers receive an x402 payment requirement...'), which helps an agent know when payment, mandate, and credential are relevant.

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 'Drain the queue of pool records routed to you,' a specific verb plus resource that clearly identifies this as a buyer-facing pull operation for routed records. The title 'Pull routed records (buyers)' reinforces the audience, and this is distinct from sibling tools like get_quotes or market_data.

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

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

It provides clear context: use when there are pool records routed to the caller, and mentions the prerequisite 'Requires your buyer key' plus a payment condition for metered buyers. It does not explicitly enumerate alternatives or when-not-to-use cases, but the queue-routing context makes the usage situation 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 valuable behavioral context beyond annotations: registration requires the licensed contact to confirm via an emailed link, and nothing is delivered before that confirmation. It also discloses the free founding period and later prepaid credit model, which are non-obvious side effects and requirements not inferable from the schema or 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 dense but every sentence serves a purpose: caller eligibility, tool purpose, required data, lead delivery options, confirmation behavior, and commercial terms. It is front-loaded with the most important selection cue and has 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 an 11-parameter registration tool with no output schema, the description covers prerequisites, required data, activation flow, delivery constraints, and pricing context. It does not describe the response shape or post-confirmation steps, but the tool is about initiating registration, and the provided context is sufficient for correct 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?

With 64% schema coverage, the description compensates by enumerating key inputs: agency name, NPN, NPN state, appointment states, licensed contact details, and delivery method. It adds meaning by explaining that the contact confirms by email and that leads can arrive via email, webhook, or pull. It does not cover all parameters such as verify_token or website, but the most decision-relevant semantics are present.

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 ('register') and a clear resource ('an insurance agency as a buyer'), and states the purpose: to receive consented consumer requests in its states. It also distinguishes this from sibling tools like get_quotes or agency_status by focusing on agency enrollment rather than quoting or status checks.

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 opening line scopes the tool to 'an AI working for a licensed insurance agency' and makes the intended use clear: register the agency to receive consented consumer requests. It does not explicitly name alternatives or exclusion cases, but the context is strong enough for an agent to select 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?

Beyond the annotations, the description discloses meaningful behavioral details: up to 4 licensed agents receive the request and contact details under contact_consumer, marketing partners can be included under sell_identity, nothing leaves without consent, deliveries and refusals are receipted, and POST /forget revokes. This gives the agent a strong model of side effects and data 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?

The description is dense but mostly earns its length, front-loading the main purpose before consent mechanics, scope, and revocation behavior. A few clauses are slightly convoluted, but every sentence contributes useful operational detail.

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 purpose, prerequisites, consent handling, scope variations, data recipients, receipts, and revocation. It does not specify the return value or output shape despite there being no output schema, but the consent flow and side effects are sufficiently described 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?

Schema description coverage is only 44%, so the description needs to compensate. It thoroughly explains the central consent object, scope, and the origin of quote_id, but it does not add meaning for optional parameters like best_time, preferred_channel, email_address, or the consent text/version fields, leaving a meaningful 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, specific action: connect the consumer with licensed insurance agents who can quote and bind. It also distinguishes itself from siblings by requiring a quote_id from get_quotes and by emphasizing the consent requirement, which makes the tool's unique role obvious.

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 identifies prerequisites: a quote_id from get_quotes, the consumer's name and phone, and explicit consent. It explains the consent pathways and scope-based behavior, giving an agent enough context to know when this tool is appropriate, though it does not explicitly enumerate when-not-to-use alternatives.

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