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 disclose readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is covered. The description adds the output scope (a status snapshot of seven statistics) but repeats the buyer-key auth requirement already present in the parameter schema, and discloses no side effects, rate limits, or other novel behaviors.
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 zero filler. The high-value field enumeration is front-loaded, and the access requirement is stated once at the end. 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?
With one fully documented required parameter and no output schema, the description's enumeration of returned fields gives an agent enough to understand what the tool provides. It is complete for a simple read-only status lookup, though domain terms (states, rail, terms) and value formats are left unexplained.
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 single buyer_key parameter is already documented as 'Issued to the licensed contact at confirmation'. The description merely restates this ('Requires the buyer key issued at confirmation') without adding format, acquisition method, or usage nuance, so the baseline 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 opens with 'For a registered agency' and enumerates the exact data returned (states, rail, terms, founding period, queued/delivered counts, credit balance, licence verification), making the resource and scope very specific and distinct from the sibling check_eligibility. However, it lacks an explicit verb such as 'gets' or 'returns', and does not directly differentiate itself from its sibling, so it stops short of 5.
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 clear context for when the tool applies ('For a registered agency') and states the prerequisite (buyer key issued at confirmation). It does not, however, mention alternatives, exclusions, or when not to use this tool relative to check_eligibility, so it is strong on context but not on routing.
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 function: eligibility, quotes, data-use disclosure, agency onboarding, status, market data, pull queue, and contact request. No two tools are substitutable, and dependencies are explicit (check_eligibility first, get_quotes produces quote_id, request_agent_contact consumes it).
Most tools follow a clear verb_noun snake_case pattern (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact). agency_status, data_use_terms, and market_data are noun phrases but are still readable and fit the domain.
Eight tools cover the full lifecycle—eligibility, quoting, consent, agency onboarding, lead delivery, and market data—without redundancy. Each tool earns its place and the count is well-scoped.
Core workflows are covered: eligibility, quotes, consent, contact requests, agency registration, status checks, pull queue, and market data. Minor gaps exist around account/credit management and explicit consent revocation (handled via /forget endpoint), but agents can work around these.