check_eligibilityCheck whether we can return quotes for a state before any personal details are collected. Call this first. Returns the states we are licensed in, what we can do in each, and how many licensed agents can take a request there.
| Name | Required | Description | Default |
|---|---|---|---|
| state | No | Two-letter US state code, e.g. NV | |
| product | Yes | auto |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful context beyond annotations: it requires no personal details and summarizes the return payload (licensed states, capabilities, agent counts), which helps an agent anticipate 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?
Three concise sentences: purpose, placement in the workflow, and return value. There is no repetition of schema information or filler, and the most important guidance ('Call this first') appears early.
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, read-only pre-check with no output schema, the description covers invocation timing, privacy condition, and a high-level return summary. It could be more precise about behavior when state is omitted or how the returned data is structured, but it gives an agent enough 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 documents state as a two-letter code and product via enum/default. The description adds no parameter-level meaning beyond mentioning 'a state', and it leaves ambiguous whether state is optional and how omission affects results. With 50% schema coverage, this is adequate but does not fully compensate.
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 ('Check') and resource ('whether we can return quotes for a state'), and positions it as a pre-personal-details step. It clearly differentiates from siblings like get_quotes by framing itself as the first call in a workflow.
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 explicitly says 'Call this first' and specifies the context (before personal details are collected). However, it does not name alternative sibling tools or state when not to use it, so it stops short of full when-to-use/when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a different part of the flow: eligibility, quotes, consent, agency registration, agency status, queue draining, data sales, and privacy terms. The descriptions are specific enough that no two tools appear to do the same thing.
Most tools use an imperative verb_noun style like get_quotes, pull_requests, and register_agency, but agency_status, data_use_terms, and market_data are noun phrases. The snake_case is consistent and readable, but the verb_noun convention is not maintained throughout.
Eight tools is well within the ideal range and each tool earns its place by covering a distinct need: consumer quoting, consent, agency lifecycle, queue processing, and data products. The set feels intentionally scoped rather than padded.
The main quote-to-consent flow and agency registration/drain flows are present, but there are notable gaps: no MCP tool for revoking consent (only a POST /forget endpoint is mentioned), and no update or deactivate operations for agencies or credits. Agents can work around some gaps, but the lifecycle is incomplete.