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 communicate readOnlyHint, idempotentHint, and non-destructiveness. The description adds value beyond annotations by disclosing what information the tool surfaces (data recipients, purpose, retention, revocation, consent wording) and noting that output is machine-readable for evaluating an exchange before committing.
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?
Two dense sentences with no filler. The opening clause front-loads the core purpose, the parallel list efficiently enumerates the covered terms, and the final sentence adds the machine-readable evaluative benefit without redundancy.
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?
With no output schema, the description partially compensates by stating the result is machine-readable, but it does not specify the exact format. Given zero parameters and safety annotations, an agent can still select and invoke the tool correctly.
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?
This tool has zero parameters and the schema covers 100% of them, so no parameter explanation is needed. The description appropriately focuses on purpose and output semantics, meeting the baseline for a no-parameter tool.
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 states precisely what the tool provides: data use terms and consent wording, naming the specific content (recipients, purpose, retention, revocation) and the sibling tool it is meant to precede (request_agent_contact). It is specific, non-tautological, and clearly distinguishes itself from the sibling list.
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 anchors usage timing: present this consent wording before request_agent_contact. It does not list exclusions or alternative tools, but the context is clear enough for an agent to know when this tool is relevant.
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 has a distinct purpose: eligibility checks, quotes, agency registration, market data, and lead retrieval are clearly separated. Even the potentially overlapping get_quotes and market_data are well-differentiated by their descriptions (individual quotes vs. de-identified dataset).
Tool names use snake_case and are mostly descriptive, but the pattern is not uniform: some are verb_noun (check_eligibility, get_quotes, pull_requests, register_agency) while others are noun_noun (agency_status, data_use_terms, market_data). This is a minor inconsistency that does not hinder readability.
With 8 tools, the set is well within the optimal 3-15 range for a focused service. Each tool addresses a necessary function for the car insurance quote and agency workflow, and none feel redundant.
The tool surface covers the core lifecycle: eligibility, quoting, agent contact, agency registration and status, data usage terms, market data access, and lead retrieval. No obvious missing operations for the stated domain are apparent.