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 mark the tool as read-only, idempotent, and non-destructive. The description adds useful behavioral context by specifying what information is disclosed and that the output is machine-readable for pre-exchange evaluation, which goes beyond the 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 information-dense, with no filler. The front-loaded phrase 'What happens to anything you send us' immediately establishes the tool's purpose, and every clause adds relevant detail.
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, the description is complete: it covers the returned content (recipients, purpose, retention, revocation, consent wording), explains machine readability, and ties the tool to a specific workflow step involving request_agent_contact.
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 schema fully defines the input. The description still helps by clarifying the semantic content of the returned terms, but no parameter-level explanation is needed.
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 what the tool provides: data-use terms, retention/revocation details, and consent wording. It avoids tautology by explaining the resource in concrete terms, though it lacks an explicit verb like 'retrieve' or 'get'.
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 to use the tool: before request_agent_contact and before making an exchange. It does not mention direct alternatives or explicit exclusions, but the intended usage moment is clearly stated.
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 step or resource: eligibility pre-check, quotes, consent/contact, agency registration, agency status, lead queue, market data, and data-use terms. Even the two quote-related tools are clearly sequenced with check_eligibility described as 'call this first'.
Five tools use a clear verb_noun pattern (check_eligibility, get_quotes, pull_requests, register_agency, request_agent_contact), but three are bare noun phrases (agency_status, data_use_terms, market_data). All names are readable and consistently snake_case, but the verb-led convention is not uniform.
Eight tools cover the main stages of an insurance marketplace: eligibility, quotes, consumer consent, agency onboarding, lead delivery, market data, and terms. The count is well-scoped and each tool earns its place.
The core consumer journey (eligibility → quotes → consented agent contact → lead delivery) and agency workflow (register → status → pull requests) are well covered, along with market data and terms. Minor gaps exist, such as no explicit update/removal for agency registration and consent revocation only mentioned as POST /forget rather than a tool.