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 |
|---|---|---|---|
No arguments | |||
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, idempotentHint, and destructiveHint=false, covering safety. The description adds value by stating the output is machine-readable and intended for evaluating an exchange before proceeding, which gives useful behavioral context for a zero-parameter read-only tool.
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, both information-dense: the first enumerates all the policy dimensions covered, and the second states machine readability and the evaluation use. No filler or repetition, with the most important details front-loaded.
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 zero-parameter, read-only tool with no output schema, the description is complete: it tells the agent what data the tool returns, why it matters, and when to call it. The exact machine-readable format is unspecified, but that is not needed for correct selection and invocation.
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 schema coverage is 100% by vacuity; there are no arguments for the description to explain. The baseline for zero-parameter tools is 4, and the description appropriately focuses on output content rather than parameters.
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 states precisely what the tool provides: machine-readable data-use terms covering recipients, purpose, retention, revocation, and exact consent wording to use before request_agent_contact. This goes well beyond the title and clearly distinguishes it from siblings like market_data or register_agency.
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 positions this as the step before request_agent_contact: the consent wording is for presenting before that call, and the machine-readable output is for evaluating the exchange first. It doesn't discuss alternatives or when-not-to-use, but the intended context is clear.
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 resource/action: eligibility check, quotes, consent terms, contact request, agency registration, agency status, queue pull, and market data. Even pull_requests and market_data are clearly separated as private consented queue vs. de-identified public dataset. 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). The names are readable and underscore-consistent, yet the mixed verb/noun convention is noticeable.
Eight tools cover the consumer quote/contact flow, agency lifecycle, queue delivery, market data, and privacy terms without bloat. Each tool earns a place and the set is within the ideal 3-15 range.
The core lifecycle is covered: eligibility, quotes, consent, contact request, agency registration, status, and pulling routed requests. Minor gaps exist—no in-MCP update/delete for agencies and no receipt/revocation tool beyond the mentioned POST /forget—but agents can work around them.