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, so the description didn't need to restate safety. The description adds useful behavioral context: it requires the buyer key issued at confirmation (an authorization credential), and it lists the specific data dimensions returned, which helps the agent understand the scope of the read operation. It could add a bit more about error conditions (e.g., invalid key, unregistered agency), but the annotations plus the detailed field list give strong transparency.
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?
The description is a single sentence that packs all essential information: the tool scope (registered agency), the exact data returned, and the credential requirement. There is no fluff or repetition of schema data. The information is front-loaded with the tool's purpose before the credential note.
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 read-only status tool with one parameter, a comprehensive output schema, and full annotations (read-only, idempotent, non-destructive), the description is nearly complete. It lists all returned data dimensions and the required credential. It doesn't describe output format or error behavior, but with no output schema and a clearly enumerated result list, an agent has enough to decide whether to call it and how to use the result. A minor gap is not mentioning what happens if the key is invalid, but this is not critical for selection.
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%, so the schema already documents the buyer_key parameter as 'Issued to the licensed contact at confirmation.' The description reinforces this by stating the key is required and was 'issued at confirmation,' adding the nuance that this key is the mechanism for accessing the status. Since there is only one parameter and it is fully covered by the schema, the description provides adequate additional meaning.
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 begins with a clear verb-like phrase ('For a registered agency') and enumerates exactly what the tool returns: states, rail, terms, founding period, queued and delivered counts, credit balance, and licence verification status. This is much more specific than the title 'Agency status (buyers)' and clearly identifies the tool as a status/read query, distinguishing it from sibling actions like check_eligibility.
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 tool is for a 'registered agency' and that it 'Requires the buyer key issued at confirmation.' This gives clear context on when to use it (when you have a registered agency and the buyer key) and signals that the buyer key is a prerequisite. It does not explicitly name alternatives or exclusions, but the parameter requirement and read-only nature provide enough guidance for an agent to select it appropriately.
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 function—eligibility, quotes, registration, status, data access, contact requests, and queue draining. Descriptions are specific enough to prevent confusion; even related tools like check_eligibility and get_quotes are clearly separated by their purposes.
All tool names use snake_case, but they mix verb-noun patterns (check_eligibility, get_quotes) with noun-only patterns (agency_status, market_data). This is readable and mostly predictable, but not fully consistent across the set.
8 tools is well-scoped for an insurance lead-generation server covering quotes, agency management, data access, and consent workflows. Each tool has a clear purpose and none are redundant.
The surface covers the core lifecycle: eligibility, quotes, contact requests, agency registration, status checks, data pulls, and data-use terms. Minor gaps exist—no update/delete for agencies, no specific quote retrieval—but agents can work around these for typical workflows.