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Glama

Oklahoma Insurance

by com.oklahomaautoquotes

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band, record, prices shown, the door and the AI vendor that asked, and the outcome: consented, verified, delivered, withdrawn).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

A4.5/5.0
Behavior5/5

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.

Conciseness4/5

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.

Completeness4/5

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.

Parameters4/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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.

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TDQS

A3.9/5.0
Disambiguation4/5

Each tool covers a distinct function: eligibility, quoting, consent terms, agency registration/status, lead pulling, market data, and agent contact. The only mild overlap is between check_eligibility and get_quotes, but their descriptions make clear one is a pre-check for licensing/state capacity while the other returns actual indicative prices.

Naming Consistency3/5

Names are uniformly snake_case and mostly follow a verb_noun pattern like check_eligibility, get_quotes, pull_requests, register_agency, and request_agent_contact. However, agency_status, data_use_terms, and market_data are noun phrases rather than actions, so the pattern is mixed but still readable.

Tool Count5/5

Eight tools is well-scoped for this insurance-agency lead and quote platform. Each tool earns its place in the workflow, with no redundant sprawl and no feeling of an underbuilt or overloaded surface.

Completeness3/5

The primary flow is covered: eligibility, quoting, consent, agent contact, agency registration/status, lead pulling, and market data. Missing lifecycle operations include updating an agency registration, managing credits/payments, and an explicit consent-revocation tool, since POST /forget is referenced but not exposed as an MCP tool.

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