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 the safety profile. The description adds value beyond annotations by disclosing the authentication requirement (buyer key) and enumerating the return contents, which is especially useful since there is no output schema. It does not mention failure behavior, but given the strong annotation coverage, this is a minor gap.
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 field enumeration is dense but purposeful. The purpose is front-loaded with the resource and the returned data. The passive 'For a registered agency: its...' construction is slightly awkward but does not cost clarity.
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 single-parameter, read-only tool with no output schema, the description compensates well by enumerating the return fields and stating the required credential. It does not specify the response format or error cases, but the combination of full schema coverage, safety annotations, and the detailed field list makes this largely complete for an agent to call 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?
Schema description coverage is 100% — the schema already describes buyer_key as 'Issued to the licensed contact at confirmation.' The description's phrase 'Requires the buyer key issued at confirmation' largely restates the schema rather than adding new meaning. No additional format, lookup, or sourcing details are provided, so the baseline of 3 applies.
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 the resource (a registered agency) and enumerates the exact data returned: states, rail, terms, founding period, queued/delivered counts, credit balance, and licence verification status. However, there is no explicit verb like 'get' or 'retrieve' — the fragment 'For a registered agency: its...' implies retrieval rather than stating it — and it does not explicitly differentiate 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 gives one concrete prerequisite — 'Requires the buyer key issued at confirmation' — which tells the agent what credential is needed to invoke the tool. However, it does not state when to prefer this tool over check_eligibility or data_use_terms, nor when not to use it, so the usage context is implied rather than explicit.
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.