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, destructiveHint=false, so the safety profile is covered. The description adds context that the tool returns machine-readable information to evaluate an exchange before committing, which is useful but does not disclose details such as response format or any external data dependencies. No contradiction exists.
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, dense sentence that packs in all key aspects: data recipients, purposes, retention, revocation, consent wording, and the evaluative purpose. It is front-loaded with the core question ('What happens to anything you send us') and avoids redundancy with annotations or schema.
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?
Given zero parameters, no output schema, and annotations covering safety, the description is complete for the agent's invocation purposes. The only minor gap is that it does not specify the exact return format or field names, but with no output schema and a natural-language description, this is acceptable. The tool's role in pre-contact consent evaluation is fully conveyed.
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 0 parameters, and schema coverage is 100% because the schema is empty. The description fully context matters because there are no parameters to document, and it clearly communicates the tool returns data-use information, which is the primary semantic needed for invocation.
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 uses a specific verb ('evaluate the exchange') and resource (data use terms and consent wording) and clearly enumerates the content: recipients, purposes, retention, revocation, and consent wording. It distinguishes itself from siblings by explicitly mentioning request_agent_contact, making its purpose unmistakable.
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 says to use this tool when needing to know data handling terms and consent wording before presenting request_agent_contact. It implies the prerequisite use case (before making an exchange) but does not explicitly contrast with all sibling tools; however, the explicit reference to a sibling provides near-complete usage 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 targets a distinct part of the workflow: eligibility, quoting, consent, agency registration, status, queue draining, and market data. There is no meaningful overlap, and descriptions clearly separate consumer-facing from agency-facing operations.
Most tools follow a verb_noun pattern like check_eligibility, get_quotes, register_agency, and pull_requests. A few are noun phrases such as agency_status, market_data, and data_use_terms, but all are lowercase snake_case and predictable.
Eight tools fit the server's scope well: the consumer quote/contact flow, agency lead handling, terms disclosure, and market data each have dedicated tools. None feel redundant, and the count is neither thin nor bloated.
The core lifecycle is well covered: check eligibility, get quotes, request agent contact, register an agency, check agency status, and pull requests. Minor gaps exist around explicit revocation/forget and agency updating, though these are hinted at in descriptions.