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Californiacarquotes

Buy de-identified demand records (the dataset)

market_data
Read-only

The demand dataset: every auto insurance request people and their AI assistants made here, de-identified (age band, ZIP3, vehicle, coverage, mileage band, record, prices shown, the door and the AI vendor that asked, and the outcome: consented, verified, delivered, withdrawn). Never a name, phone, email, date of birth or five-digit ZIP. Filter by state, since, until, door, vendor; limit and offset. Priced per record with a minimum per pull; pay with a prepaid buyer key, x402, MPP or AP2. Without payment the result is a 402 with the price and a preview count. A free sample and the schema are at /data.json.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doorNo
limitNo
sinceNo
stateNo
untilNo
offsetNo
vendorNo
mandateNoAP2 Payment Mandate
paymentNox402 X-PAYMENT payload
buyer_keyNoA prepaid buyer key
credentialNoMPP credential

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations indicate readOnly=true and destructive=false, and the description reinforces this by describing a read-only purchase of data. It adds meaningful behavioral context beyond annotations: the pricing requirement, the 402 response without payment, the de-identification guarantees, and the availability of a sample/schema. The only minor gap is not describing pagination behavior or response structure in detail, but the description discloses the important failure mode.

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

Conciseness5/5

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

The description is a dense, front-loaded paragraph that conveys the dataset composition, privacy guarantees, filtering options, pricing model, payment methods, and failure behavior in a few sentences. Every sentence adds operational value. The only minor inefficiency is the slightly long list of fields, but it is necessary to convey what the dataset contains.

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

Completeness4/5

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

For a read-only data access tool with 11 optional parameters and no output schema, the description covers the key operational aspects: what data is returned, privacy exclusions, filtering, pagination via limit/offset, payment requirements, error behavior (402), and a pointer to the sample/schema. It could be more complete by explaining how the 'mandate' and 'credential' parameters interact with the payment methods, and by describing the exact response format, but the essentials are present.

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

Parameters3/5

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 compensate for undocumented parameters. It does mention several parameters (state, since, until, door, vendor, limit, offset) and payment methods (buyer_key, x402, MPP, AP2), which maps to some of the 11 parameters. However, parameters like 'mandate' and 'credential' are only minimally described in the schema and not explained in the description; an agent might not know exactly how to construct the payment payload. The description adds value but does not fully compensate for the low schema coverage.

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

Purpose5/5

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

Description clearly states the tool returns a de-identified demand dataset of auto insurance requests, enumerates the fields, and explicitly emphasizes what is excluded (names, emails, five-digit ZIPs). The title 'Buy de-identified demand records' and description together distinguish this from siblings like get_quotes or pull_requests by focusing on the historical dataset rather than quotes or pulls.

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

Usage Guidelines4/5

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

The description provides explicit context: users can filter by state, since, until, door, vendor; limit and offset; and pay via prepaid buyer key, x402, MPP or AP2. It also states what happens without payment (402 with price and preview count) and where to find a free sample. It doesn't explicitly say when to prefer this over siblings, but the dataset scope is clearly defined enough for an agent to infer appropriate use.

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

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a distinct purpose: eligibility checks, quotes, agency registration, market data, and lead retrieval are clearly separated. Even the potentially overlapping get_quotes and market_data are well-differentiated by their descriptions (individual quotes vs. de-identified dataset).

Naming Consistency4/5

Tool names use snake_case and are mostly descriptive, but the pattern is not uniform: some are verb_noun (check_eligibility, get_quotes, pull_requests, register_agency) while others are noun_noun (agency_status, data_use_terms, market_data). This is a minor inconsistency that does not hinder readability.

Tool Count5/5

With 8 tools, the set is well within the optimal 3-15 range for a focused service. Each tool addresses a necessary function for the car insurance quote and agency workflow, and none feel redundant.

Completeness5/5

The tool surface covers the core lifecycle: eligibility, quoting, agent contact, agency registration and status, data usage terms, market data access, and lead retrieval. No obvious missing operations for the stated domain are apparent.

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