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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is established. The description adds useful context about the timing ('Call this first') and the output scope (licensed states and agent counts), but does not disclose details like whether the list of states is static or affected by dynamic factors. With annotations covering core safety, a 3 is appropriate.
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?
The description is two sentences long, with the key usage instruction front-loaded and the return value summary placed at the end. Every clause earns its place without redundancy.
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?
Given the tool is a read-only pre-check with full schema parameter coverage and rich annotations, the description is nearly complete. It could theoretically detail pagination or the exact structure of the returned list, but for an agent deciding whether to call this tool first, nothing important is missing.
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 100% — both parameters (`state` and `product`) are documented in the schema. The description adds a useful hint that this is the pre-collection step, but does not further elaborate parameter semantics beyond the schema. Baseline 3 is correct when the schema does the heavy lifting.
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 clearly states what the tool does: 'Check whether we can return quotes for a state before any personal details are collected.' It specifies the resource (states eligibility for quotes) and the verb (check), and distinguishes it from typical quote-collection tools by emphasizing it is called first before personal details.
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 description explicitly instructs 'Call this first,' establishing a clear usage position in a workflow. It also describes what the tool returns — licensed states, capabilities per state, and agent counts — which helps the agent decide when to use it versus alternatives such as fetching or submitting quotes.
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 serves a clearly distinct function: eligibility checks, quotes, agent contact, agency registration and status, data pulls, terms, and market data. Even where agency_status and check_eligibility both relate to licensing, their purposes are sharply separated by the descriptions.
Most tools follow a verb_noun pattern like check_eligibility, get_quotes, pull_requests, register_agency, and request_agent_contact. Three resource-style names (agency_status, data_use_terms, market_data) deviate slightly but remain readable and predictable.
Eight tools are well-scoped for this insurance lead and quote platform. Each tool covers a meaningful part of the workflow without unnecessary redundancy or bloat.
The core lifecycle is covered: eligibility, quoting, consent, agency registration, lead delivery, status, terms, and market data. Minor gaps exist, such as no update/delete for agency registration and no explicit tool to revoke consent, but these are workable via the described REST endpoints.