agency_statusFor a registered agency: its states, rail, terms, founding period, queued and delivered counts, credit balance, and whether the licence was verified. Requires the buyer key issued at confirmation.
| Name | Required | Description | Default |
|---|---|---|---|
| buyer_key | Yes | Issued to the licensed contact at confirmation |
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 safety. The description adds valuable behavioral context by disclosing the authentication requirement (buyer key issued at confirmation) and clarifying that licence verification status is part of the response. 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?
A single sentence packs a clear enumeration of return fields with no filler. The data scope is front-loaded, and the credential requirement is placed at the end. Every word 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 description compensates for the absence of an output schema by enumerating the main return categories (states, rail, terms, founding period, queued/delivered counts, credit balance, verification). With one fully documented parameter and a safe annotation profile, an agent has enough to invoke the tool correctly. Details like error cases are absent but minor.
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 coverage is 100%, and the schema already documents buyer_key as 'Issued to the licensed contact at confirmation.' The description restates that the key is required and issued at confirmation, adding minimal new meaning. Baseline 3 is appropriate when the schema carries the parameter documentation burden.
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 identifies a status/read operation for a registered agency and enumerates the returned data: states, rail, terms, founding period, counts, credit balance, and licence verification. It lacks an explicit verb like 'retrieves' or 'returns,' but the phrasing 'For a registered agency: its...' makes the operation unambiguous. The title reinforces the scope, and the field list distinguishes it from sibling tools.
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 states the precondition (registered agency) and the required credential (buyer key), which implies when to use this tool. However, it does not explicitly contrast it with siblings like check_eligibility or get_quotes, nor does it state when not to use it. The context is clear, but exclusions and alternatives are left to inference.
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 workflow phase: eligibility, quoting, consent, agency registration, lead pulling, status, dataset purchase, and data-use disclosure. Even market_data and pull_requests, which both involve buyer keys and records, are clearly separated as an analytics dataset versus a lead queue. 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). All names are readable and consistently snake_case, but the set mixes action-oriented verbs with static resource names, so it is not fully consistent.
Eight tools is well within the ideal range and each one covers a necessary part of the insurance quote and lead workflow. No tool feels redundant, and the scope is neither bloated nor thin.
The set covers the core lifecycle: check eligibility, produce quotes, obtain consent, request agent contact, register agencies, pull lead records, inspect status, review data-use terms, and buy market data. Minor gaps exist, such as an explicit tool for updating agency details or revoking consent via POST /forget, but agents can complete the main workflows.