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=true, idempotentHint=true, and destructiveHint=false. The description adds value beyond annotations by disclosing that the output is machine-readable and intended for pre-exchange evaluation, and that it includes human revocation mechanics. No contradiction with 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 two substantive sentences are efficient and front-loaded with the scope. However, the description as provided ends with extraneous trailing text ('Respond with JSON only.'), which appears to be a prompt artifact rather than authored content, so it forfeits a perfect score.
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, no-output-schema informational tool, the description covers what content is returned, why it is useful, and when to call it relative to request_agent_contact. Minor gaps: it does not state a concrete return shape or format beyond 'machine readable,' but the tool's simplicity makes that acceptable.
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 and schema coverage is 100% (vacuously), so the baseline of 4 applies. There are no parameters for the description to document, and it correctly adds no misleading param information.
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: data use terms covering recipients, purposes, retention, revocation, and consent wording. It links to the sibling request_agent_contact, distinguishing its role. The operation verb is implicit rather than explicit ('retrieves'/'provides'), which keeps it from a 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 a concrete usage context: present this consent wording before request_agent_contact, and evaluate the exchange before making it. It does not explicitly name exclusions or alternatives, but the directive to use it pre-exchange is clear. A 5 would require explicit when-not-to-use guidance.
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 maps to a distinct job: eligibility, quoting, consent/terms, agency registration, status, lead pull, market data, and contact request. The two data-delivery tools (market_data and pull_requests) are clearly separated by de-identified versus consented/identified content.
Five tools follow an imperative verb_noun style (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact), while three are noun-only resource names (agency_status, data_use_terms, market_data). All are readable snake_case, but the verb/noun split prevents a single predictable pattern.
Eight tools cover both consumer-facing and agency-facing sides of the quote/contact platform without redundancy. Each tool has a distinct role, and the count is squarely in the well-scoped range.
The core lifecycle is covered: eligibility, quotes, consent, agency registration, status, lead delivery, and market data. Gaps are update/delete or revocation operations (e.g., POST /forget is referenced but not exposed as a tool, and agency details cannot be updated), but agents can complete the main workflows.