Georgia Insurance

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

Annotations already cover read-only, idempotent, and non-destructive behavior, so the description's job is lighter. It adds value by explaining that the buyer key must be the one issued at confirmation, which is an access-related nuance beyond the annotations. It does not go deeper into edge cases like unregistered agencies or error 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?

A single sentence packs the resource, the full set of returned fields, and the authentication requirement. Nothing is wasted, and the essential content is front-loaded.

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

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

For a simple read-only status tool, the description lists the returned fields, identifies the required key, and is supported by clear annotations and a fully covered schema. A minor gap is that it doesn't specify behavior when the agency is not registered or the key is invalid, but the tool's low complexity makes this acceptable.

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 schema already explains the buyer_key as issued to the licensed contact at confirmation. The description repeats that requirement rather than adding new parameter meaning. With full schema coverage, a baseline score of 3 is appropriate.

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 resource (a registered agency) and enumerates the data returned (states, rail, terms, founding period, counts, credit balance, licence verification). It reads as a status/retrieval tool. It doesn't explicitly use a verb like 'retrieves', but the meaning is clear enough to distinguish it from sibling tools like register_agency or request_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?

The description implies this tool is for checking agency status after registration, and it notes the buyer key requirement, but it gives no explicit guidance on when to prefer it over siblings such as check_eligibility or data_use_terms. There are no exclusions or alternative routing signals.

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 already establish read-only, idempotent, and non-destructive behavior, so the description does not need to restate safety. It adds value by disclosing the response contents: licensed states, per-state capabilities, and licensed agent counts. It also implies an operational trait that the state parameter is optional, since it says 'states we are licensed in' rather than only the requested state.

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

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

Two sentences carry the full definition with no filler. The primary action and sequencing instruction are front-loaded, and the return value summary is packed into a single efficient sentence that adds real decision-relevant information.

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

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

For a low-complexity tool with no output schema, the description covers purpose, when to call, and the high-level return shape. It does not specify the exact response format or behavior for invalid or unsupported states, but the decision-relevant facts—licensing, capabilities, and agent availability—are all present.

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 description adds meaning to the state parameter by implying it can be omitted to return all licensed states. However, the product parameter is not mentioned at all, and with schema coverage at 50%, the description only partially compensates. The omission of product is minor because the schema marks it as required with a single 'auto' enum value, but it still leaves an undocumented parameter.

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 'Check whether we can return quotes for a state', naming a specific verb, resource, and precondition. It clarifies the tool's role as a pre-check that happens before any personal details are collected, and it previews what the result will contain, making the purpose 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?

'Call this first' provides explicit and actionable sequencing guidance, and 'before any personal details are collected' defines the condition under which this tool is the right choice. No alternatives or exclusions are named, but with no sibling tools provided in the context, the guidance is sufficiently clear for agent selection.

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, covering the safety profile. The description adds useful behavioral context beyond that: the exact content domains returned (who receives, purpose, retention, revocation, consent wording) and the machine-readable format. Nothing contradicts 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?

Two dense sentences with zero filler. The first sentence front-loads the full list of content areas the tool returns, and the second immediately conveys the practical use case and machine-readability. 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 zero-parameter read-only tool, the description is complete: it enumerates all return content categories, gives the exact timing for use (before request_agent_contact), states the format is machine readable, and annotations cover the safety profile. Nothing an agent needs to invoke it correctly is missing.

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 zero parameters and 100% schema description coverage, there is nothing for the description to explain. The baseline for 0 params is 4, and the description adds relevant context by specifying what the terms apply to ('anything you send us' / 'the exchange'), which helps an agent understand the tool's inputs without 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 states what the tool provides: data use terms covering recipients, purpose, retention, revocation, and consent wording. It distinguishes itself from siblings by explicitly referencing request_agent_contact as the action that requires this tool's consent wording, so an agent can tell it apart without inspecting the schema.

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

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

The description gives an explicit usage trigger: present this consent wording before request_agent_contact, and evaluate the exchange before making it. This is clear context for when to invoke the tool. It doesn't name alternative tools or exclusions, but with zero parameters and a unique informational role among siblings, there are no competing tools to rule out.

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 set readOnlyHint to true, yet the description states that an unlicensed request 'is still registered and a quote_id returned' — implying a side effect or resource creation. This contradicts the read-only annotation. The additional privacy and dynamic-request details are useful, but the contradiction is severe.

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: core purpose, data-minimization rule, unlicensed fallback, and dynamic missing-facts behavior. The main action is front-loaded with 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?

The description covers the main output, licensing constraints, fallback behavior, and privacy boundaries, and the schema defines required fields. However, the contradiction between the readOnlyHint annotation and the 'request is still registered' behavior leaves operational expectations ambiguous, and no explicit sibling routing is provided.

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 some burden. It adds useful meaning by stating the inputs are limited to rating facts and that missing facts are requested dynamically. However, it does not define the individual parameters such as prior_continuous, violations_3yr, or annual_mileage beyond their names.

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: 'Return indicative auto insurance prices from multiple carriers.' It also clarifies the licensing scope and the rating-facts-only nature of the tool, which distinguishes it from non-quoting siblings like register_agency or request_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?

The description gives clear context for when the tool is appropriate: generating indicative quotes using only rating facts, plus a fallback behavior when the entity is not licensed. However, it does not explicitly name sibling alternatives or state when to prefer them, so it stops short of full 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?

Well beyond the readOnly and non-destructive annotations, the description discloses the payment gating behavior ('Without payment the result is a 402 with the price and a preview count'), the pricing model with minimum per pull, the accepted payment methods, strict privacy redaction guarantees, and where a free sample lives. The 402 detail in particular is a concrete behavioral trait an agent would not otherwise know.

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?

A single dense paragraph that flows from dataset contents to privacy to filtering to pricing to failure mode to sample location, with every clause earning its place. The core 'what' is front-loaded, and nothing repeats the schema or annotations.

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 paid data tool with no output schema, the description covers the dataset contents, privacy guarantees, filtering and pagination controls, pricing and minimum-pull terms, every payment method, the unpaid-request fallback (402 plus preview count), and a schema endpoint. The only absences (date formats, valid state values) are minor and are addressed by the offered /data.json sample.

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 coverage at only 36%, the description compensates by naming the filter dimensions (state, since, until, door, vendor; limit and offset) and explaining what door and vendor mean within the dataset, while also giving the payment params (buyer key, x402, MPP, AP2) concrete meaning. It stops short of value formats — since/until date syntax or valid state values are left to inference — so compensation is strong but not exhaustive.

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 the verb 'Buy' and the resource 'de-identified demand records', and the description pins the scope precisely: 'every auto insurance request people and their AI assistants made here, de-identified...'. Among siblings like get_quotes, check_eligibility, and pull_requests, this is unmistakably the paid dataset purchase tool, distinguished by its detailed dataset inventory and payment mechanics.

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?

Usage is implied: an agent wanting auto-insurance demand records with payment attached would select this tool, and the description gives operational guidance (filters, payment methods, sample endpoint). However, it never explicitly states when to prefer this tool over siblings or when not to use it, so no exclusion or alternative routing is provided.

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 provide readOnlyHint=false, so the description carries the burden of explaining mutation. It clearly discloses destructive behavior ('Drain the queue'), the 402/payment flow for metered buyers, and the scope-projection guarantee ('never wider'). This is valuable behavioral 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?

Three sentences with no filler. The primary action is front-loaded in the first sentence, followed by prerequisites and behavioral details. 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 simple queue-drain operation with a 4-param schema, no output schema, and minimal annotations, the description covers the action, prerequisites, payment state, and authorization scope. It does not describe the response shape or empty-queue behavior, but those are secondary for this operation.

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%, so the schema already documents mandate, payment, and credential. The description adds context for buyer_key (required) and explains when payment is needed (status 402 with accepts[]), but it does not add meaning for mandate or credential. It partially compensates for the undocumented buyer_key but does not fully expand on all 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 uses a specific verb ('Drain') and resource ('queue of pool records routed to you'), and the title's '(buyers)' scoping makes the intended audience clear. However, it does not explicitly name sibling tools or contrast itself, so differentiation relies on the domain rather than explicit comparison.

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 implies when to use this tool: when you are a buyer with a queue of routed records and need to drain it. It also states prerequisites (buyer key) and a payment condition for metered buyers, but it does not provide when-not-to-use guidance or reference alternatives like get_quotes or register_agency.

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 several non-obvious behaviors beyond the annotations: the licensed contact must confirm through an emailed link, nothing is delivered before that confirmation, and the early pricing model includes a free founding period followed by prepaid credits. These details are not present in the annotations and materially affect how an agent should expect the tool to behave. There is no contradiction with 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 front-loaded with the purpose and then moves through prerequisites, confirmation gating, and terms. It is somewhat long because it summarizes parameters already present in the schema, but every sentence contributes useful context such as delivery timing and pricing. There is no filler or repetition from annotations.

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 covers the essential prerequisites, the confirmation step, what is gated on confirmation, and points to /join for terms. It does not describe the expected return value or error scenarios, but the absence of an output schema and the reference to /join mitigate those omissions. It is reasonably complete for guiding an agent through 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 description coverage, the schema already documents most parameters. The description adds a useful summary of required groups (agency name, NPN, states, contact, lead delivery) and connects the licensed contact to the confirmation-by-email flow, but it does not explain website, webhook_url, or verify_token beyond what the schema provides. The added value is moderate.

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 identifies the resource ('the agency') and the purpose ('to receive consented consumer requests in its states'). This clearly differentiates it from sibling tools like agency_status or check_eligibility, which are not registration actions. The title reinforces the same message.

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 the intended audience ('For an AI working for a licensed insurance agency') and lays out prerequisite inputs, so an agent can infer when registration is needed. However, it never explicitly contrasts with sibling tools such as agency_status or check_eligibility, nor does it state when not to use this tool. The usage guidance remains implied rather than direct.

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 reveals important side effects: up to four licensed agents receive the request and contact details under contact_consumer, sell_identity permits sharing with marketing partners, nothing leaves without consent, every delivery and refusal is receipted, and POST /forget revokes. This gives an agent a strong model of the real-world impact of calling the 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?

The definition is dense but efficient: it front-loads the core purpose, then layers prerequisites, consent mechanics, scope behavior, and side effects without repetition or filler. Every clause adds information an agent needs to invoke the tool correctly.

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 9 parameters, nested objects, and no output schema, the description supplies the most critical context: prerequisites, consent handling, scope semantics, side effects, and revocation. It falls short only by not describing the exact response shape and by leaving several optional parameters unexplained.

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

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

The description adds meaning by sourcing quote_id from get_quotes and by explaining the consent and scope semantics. However, with schema coverage at 44%, it does not compensate for the undocumented optional parameters like best_time, email_address, preferred_channel, or the full_name context, and it does not explain verify_token's role beyond what the schema already says.

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 an explicit action and outcome: 'Connect the consumer with licensed insurance agents who can quote firm and bind,' which clearly states the resource and purpose. It is unambiguous and unlikely to be confused with sibling lookup/registration tools, though it does not explicitly compare itself to them.

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 concrete prerequisites: the quote_id must come from get_quotes, and the consumer's name, phone, and explicit consent are required. It also explains scope-dependent behavior and consent modalities. It lacks explicit when-not-to-use guidance or named alternatives, but the context and sequencing are clear.

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