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 establish that the tool is read-only, idempotent, and non-destructive. The description adds useful behavioral context by specifying exactly what the tool returns—licensed states, per-state capabilities, and licensed agent counts—without contradicting any annotation.
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 deliver the tool's purpose, the call order, and the return contents with no wasted words. The critical instruction 'Call this first' is front-loaded, making the description easy to scan and act on.
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?
Despite having no output schema, the description explains the response shape (licensed states, actions available, agent counts) and the correct usage context. For a simple eligibility-checking tool with read-only annotations, this is sufficient for an agent 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 describes the state parameter, and product is self-explanatory with a single enum value and default. The description reinforces the state-centric behavior but does not clarify the optionality of state or the product parameter's role, leaving some burden unmet at 50% schema coverage.
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: it checks whether quotes can be returned for a state before collecting personal details. It also identifies the tool's distinct output—licensed states, capabilities per state, and agent counts—clearly separating it from quote retrieval and contact-request siblings.
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 imperative 'Call this first' gives explicit sequencing guidance, and 'before any personal details are collected' clarifies the intended context. It does not explicitly name when not to use the tool or point to an alternative, so it falls just short of full alternative-routing 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 targets a distinct resource/action: eligibility check, quotes, consent terms, contact request, agency registration, agency status, queue pull, and market data. Even pull_requests and market_data are clearly separated as private consented queue vs. de-identified public dataset. No two tools appear to do the same thing.
Five tools follow a verb_noun pattern (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact), but three are noun phrases (agency_status, data_use_terms, market_data). The names are readable and underscore-consistent, yet the mixed verb/noun convention is noticeable.
Eight tools cover the consumer quote/contact flow, agency lifecycle, queue delivery, market data, and privacy terms without bloat. Each tool earns a place and the set is within the ideal 3-15 range.
The core lifecycle is covered: eligibility, quotes, consent, contact request, agency registration, status, and pulling routed requests. Minor gaps exist—no in-MCP update/delete for agencies and no receipt/revocation tool beyond the mentioned POST /forget—but agents can work around them.