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, covering the safety profile. The description adds value beyond that by explaining what the check returns (licensed states, per-state capabilities, licensed agent counts), giving the agent a clear picture of the tool's behavior without contradicting the 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 primary purpose is front-loaded, followed by the call-ordering instruction and a concise summary of return values. Every clause 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?
The tool is simple (2 params, no output schema), and the description covers purpose, call ordering, and return shape (licensed states, capabilities, agent counts). Nothing an agent needs to call it correctly is missing; the description compensates fully for the lack of an output schema.
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%: state has a clear description, and product is constrained to a single enum value with a default, so it is self-explanatory. The description adds minimal parameter-specific meaning, only tying 'state' to the notion of checking a state. This is adequate but does not go beyond the schema in any meaningful way.
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 whether') with a clear resource ('we can return quotes for a state') and adds a distinctive scope: 'before any personal details are collected.' This clearly separates it from sibling tools like get_quotes, which presumably performs the actual quote return after eligibility is confirmed.
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?
'Call this first' is explicit, direct guidance on when to invoke this tool relative to other steps. The phrase 'before any personal details are collected' further clarifies the usage window. It does not name alternatives or state when not to use it, but the sequencing instruction is strong and unambiguous.
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 has a distinct purpose: eligibility checks, quotes, agency registration, market data, and lead retrieval are clearly separated. Even the potentially overlapping get_quotes and market_data are well-differentiated by their descriptions (individual quotes vs. de-identified dataset).
Tool names use snake_case and are mostly descriptive, but the pattern is not uniform: some are verb_noun (check_eligibility, get_quotes, pull_requests, register_agency) while others are noun_noun (agency_status, data_use_terms, market_data). This is a minor inconsistency that does not hinder readability.
With 8 tools, the set is well within the optimal 3-15 range for a focused service. Each tool addresses a necessary function for the car insurance quote and agency workflow, and none feel redundant.
The tool surface covers the core lifecycle: eligibility, quoting, agent contact, agency registration and status, data usage terms, market data access, and lead retrieval. No obvious missing operations for the stated domain are apparent.