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, so the safety profile is covered. The description adds useful context about the machine-readable output and the consent-evaluation purpose, but it does not disclose return format details; that is acceptable given the annotations carry most of the burden.
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?
A single dense sentence that front-loads the resource, then efficiently enumerates the content list with a colon and commas. Every phrase adds information, and no space is wasted.
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 informational lookup with annotations covering safety, the description tells an agent what it will receive and when to call it. It stops short of specifying the exact response shape, but 'machine readable' plus the enumerated categories is sufficient for this tool's simplicity.
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 100% schema coverage, so there is nothing for the description to add. Per the baseline for 0-parameter tools, this is correct.
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 resource ('anything you send us') and enumerates the exact content categories the tool returns: recipients, purpose, retention, revocation, and consent wording. It also distinguishes itself from siblings by explicitly tying its output to request_agent_contact, so an agent can tell it apart from the other data-lookup tools.
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 clearly signals when to use it: before request_agent_contact, to prepare consent wording and evaluate the data exchange. It does not explicitly name alternatives or state when not to use it, but the targeted reference to a sibling tool gives clear contextual routing.
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 clearly distinct operation: eligibility preflight, quote generation, consumer contact, agency registration/status, queue draining, market data, and terms. Potential proximity between check_eligibility and get_quotes is resolved by explicit sequencing and different outputs.
Most tools follow a verb_noun pattern such as check_eligibility, get_quotes, register_agency, pull_requests, and request_agent_contact. However, agency_status, market_data, and data_use_terms are noun-style resource names, creating a noticeable but still readable mix.
Eight tools is well within the ideal range for a domain-focused server. Each tool serves a distinct function across quoting, consent, agency operations, queue handling, and data access, with no obvious redundancy.
Core workflows are well covered: eligibility, indicative quotes, consumer-agent connection, agency onboarding/status, lead queue draining, and market data purchase. Minor gaps remain, such as no tool to update or remove an agency, and no tool to inspect an individual consumer request or consent status; revocation is only mentioned as an external POST endpoint.