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, covering the safety profile. The description adds valuable behavioral context: it names the exact return content (licensed states, permitted actions, licensed agent counts) and the privacy-sensitive placement of the call. This goes beyond what annotations alone provide.
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 tightly written sentences with no filler. The core purpose is front-loaded, the usage directive ('Call this first') is immediate, and the return summary is compact. 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 read-only tool with two parameters and rich annotations, the description covers purpose, timing, and return payload. The only notable gap is ambiguity about whether the response is scoped to the requested state or lists all licensed states, which could affect an agent's expectations of the output.
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 50%: the state parameter is documented, but product relies only on its enum and default. The description reinforces the meaning of state by tying it to the eligibility check, but it does not clarify the product parameter or its role. This partial compensation merits a mid-range score.
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 ('Check') and identifies the resource ('return quotes for a state') and its boundary ('before any personal details are collected'). It clearly conveys the tool's purpose, but it does not explicitly distinguish this tool from sibling tools such as agency_status or get_quotes, relying instead on the unique pre-collection angle.
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 instruction 'Call this first' provides clear ordering context, and the phrase 'before any personal details are collected' sets the intended use case. However, it does not explicitly state when to avoid this tool or recommend an alternative sibling, so it stops short of full when/when-not guidance.
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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Each tool maps to a distinct stage in the insurance workflow: eligibility, quoting, consumer contact, agency registration, queue draining, account status, data purchase, and privacy terms. No two tools could reasonably be confused for the same action.
Most tools follow a clear snake_case verb_noun pattern such as check_eligibility, get_quotes, register_agency and pull_requests. A few noun-oriented names like agency_status, data_use_terms and market_data deviate slightly, making the set readable but not perfectly uniform.
Eight tools is well-scoped for the stated consumer-and-agency insurance workflow. Each tool covers a meaningful capability without redundancy or excessive fragmentation.
The tool set covers the core lifecycle: eligibility checks, indicative quotes, consumer consent and agent contact, agency registration, request queue draining, account status, market data access, and data-use terms. Minor gaps such as an explicit consent-revocation tool or agency-profile update are workaroundable through external endpoints, so agents are not left at a dead end.