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 the tool read-only, idempotent, and non-destructive. The description adds useful behavioral context: no personal details are collected, and the tool returns licensing scope, permitted actions, and agent counts. There is no contradiction with annotations.
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 with no filler. The purpose and ordering come first, followed by a concise summary of the return values. 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 two-parameter read-only tool, the description covers invocation order, output contents, and the absence of personal-data collection. It slightly underspecifies whether an omitted state returns all states versus a single state, but the schema's optional state parameter makes this inferable.
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 documents state's format and product's enum/default, so the description does not need to repeat them. At 50% schema description coverage, the description does not compensate for the undocumented product parameter, but product is fully constrained to 'auto' with a default, limiting practical risk.
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 quotes can be returned for a state), and clarifies it happens before any personal details are collected. It also names the key output, making the tool's role unmistakable and distinct from quote-fetching 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 description explicitly says 'Call this first,' so an agent knows this is the entry point. It also explains the prerequisite timing (before personal details are collected), though it does not name alternative sibling tools or provide 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 distinct part of the workflow: eligibility, quoting, consent, agency registration, status, queue draining, and market data. There is no meaningful overlap, and descriptions clearly separate consumer-facing from agency-facing operations.
Most tools follow a verb_noun pattern like check_eligibility, get_quotes, register_agency, and pull_requests. A few are noun phrases such as agency_status, market_data, and data_use_terms, but all are lowercase snake_case and predictable.
Eight tools fit the server's scope well: the consumer quote/contact flow, agency lead handling, terms disclosure, and market data each have dedicated tools. None feel redundant, and the count is neither thin nor bloated.
The core lifecycle is well covered: check eligibility, get quotes, request agent contact, register an agency, check agency status, and pull requests. Minor gaps exist around explicit revocation/forget and agency updating, though these are hinted at in descriptions.