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 meaningful context beyond these: it reveals that the tool is machine-readable, that it supports evaluating an exchange before it is made, and that it covers revocation and consent wording. This is appropriate behavioral context for a read-only, non-destructive 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?
The description is a single, information-dense sentence that front-loads the core purpose ('What happens to anything you send us') before enumerating the specifics. Every clause earns its place, though the phrasing is slightly list-like and could be tightened without losing information.
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 covers the main content areas well. It includes the key behavioral signal (evaluate before making the exchange) and the linkage to request_agent_contact. It does not specify the exact format or structure of the machine-readable output, but the absence of parameters and presence of annotations reduce the need for that detail.
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 there is no parameter semantics burden. The description fully describes what the tool returns conceptually (terms, recipients, purpose, retention, revocation, consent wording), so even with no output schema, an agent knows what to expect. Baseline 4 applies for zero-parameter tools.
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 names a precise resource: the data use terms and consent wording attached to anything sent to the service. It enumerates concrete contents (recipients, purpose, retention, revocation, consent wording), making its scope unmistakable and distinguishable from siblings like request_agent_contact.
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 states the intended use: present the consent wording before request_agent_contact and evaluate the exchange before making it. It does not explicitly name alternatives or exclusions, but the context and clear linkage to request_agent_contact provide sufficient direction for when to use the tool.
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 serves a clearly distinct function: eligibility checks, quotes, agent contact, agency registration and status, data pulls, terms, and market data. Even where agency_status and check_eligibility both relate to licensing, their purposes are sharply separated by the descriptions.
Most tools follow a verb_noun pattern like check_eligibility, get_quotes, pull_requests, register_agency, and request_agent_contact. Three resource-style names (agency_status, data_use_terms, market_data) deviate slightly but remain readable and predictable.
Eight tools are well-scoped for this insurance lead and quote platform. Each tool covers a meaningful part of the workflow without unnecessary redundancy or bloat.
The core lifecycle is covered: eligibility, quoting, consent, agency registration, lead delivery, status, terms, and market data. Minor gaps exist, such as no update/delete for agency registration and no explicit tool to revoke consent, but these are workable via the described REST endpoints.