data_use_termsWhat happens to anything you send us: who receives it, for what purpose, how long it is kept, how your human revokes it, and the exact consent wording to present before request_agent_contact. Machine readable so you can evaluate the exchange before making it.
| Name | Required | Description | Default |
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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?
The description adds meaningful behavioral context beyond the annotations: it details the categories of information returned (recipients, purpose, retention, revocation, consent wording) and states the output is machine-readable. It does not contradict the readOnlyHint, idempotentHint, or destructiveHint 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?
The description is compact and front-loaded with the core purpose. Each phrase in the list adds a distinct piece of information, and the second sentence justifies the machine-readable format. There is minimal waste.
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 no output schema and no parameters, the description carries the burden of explaining what the agent will receive. It enumerates the content areas well and notes machine readability. It could be slightly more explicit about the exact return format, but for a zero-parameter read-only tool, this is adequate.
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?
The tool has zero parameters, so the baseline is 4. The description further helps by explaining what kind of content the tool provides, which is more than necessary given the empty input schema.
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 explains what the tool provides: data use terms covering recipients, purpose, retention, revocation, and consent wording. It also names a sibling (request_agent_contact) to clarify its role, though it lacks an explicit action verb like 'retrieve' or 'return'.
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 explicitly says to use the consent wording 'before request_agent_contact' and that the data is for evaluating the exchange before making it. This gives a clear when-to-use context, though it does not mention when not to use the tool or alternative tools.
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 different part of the flow: eligibility, quotes, consent, agency registration, agency status, queue draining, data sales, and privacy terms. The descriptions are specific enough that no two tools appear to do the same thing.
Most tools use an imperative verb_noun style like get_quotes, pull_requests, and register_agency, but agency_status, data_use_terms, and market_data are noun phrases. The snake_case is consistent and readable, but the verb_noun convention is not maintained throughout.
Eight tools is well within the ideal range and each tool earns its place by covering a distinct need: consumer quoting, consent, agency lifecycle, queue processing, and data products. The set feels intentionally scoped rather than padded.
The main quote-to-consent flow and agency registration/drain flows are present, but there are notable gaps: no MCP tool for revoking consent (only a POST /forget endpoint is mentioned), and no update or deactivate operations for agencies or credits. Agents can work around some gaps, but the lifecycle is incomplete.