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 |
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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 description is not burdened with basic safety disclosure. It adds valuable behavioral context: the tool returns machine-readable data-use terms covering recipients, purpose, retention, revocation, and consent wording, which is far more informative than the annotations alone.
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 two sentences with no filler. It front-loads the core concept, enumerates the specific contents, and closes with the practical use case. Every clause earns its place and improves an agent's ability to decide when to call it.
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 informational tool, this description is complete. It specifies what the tool returns, why it matters, and when to invoke it relative to request_agent_contact. No output schema exists, but the description sufficiently conveys the tool's value and usage context.
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 schema coverage is 100%, so there is nothing for the description to add about individual parameters. The baseline of 4 applies here, and the description provides no conflicting or misleading parameter information.
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 ('what happens to anything you send us') and a concrete resource (data use terms and consent wording), explicitly listing the dimensions covered: recipients, purpose, retention, revocation, and consent wording. It also distinguishes itself from siblings by tying itself to request_agent_contact, making its role clear.
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 states when to use the tool: before request_agent_contact and before making an exchange, to evaluate data handling terms. It does not explicitly name alternatives or exclusion conditions, but the contextual guidance is sufficient for an agent to route correctly.
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 covers a distinct function: eligibility, quoting, consent terms, agency registration/status, lead pulling, market data, and agent contact. The only mild overlap is between check_eligibility and get_quotes, but their descriptions make clear one is a pre-check for licensing/state capacity while the other returns actual indicative prices.
Names are uniformly snake_case and mostly follow a verb_noun pattern like check_eligibility, get_quotes, pull_requests, register_agency, and request_agent_contact. However, agency_status, data_use_terms, and market_data are noun phrases rather than actions, so the pattern is mixed but still readable.
Eight tools is well-scoped for this insurance-agency lead and quote platform. Each tool earns its place in the workflow, with no redundant sprawl and no feeling of an underbuilt or overloaded surface.
The primary flow is covered: eligibility, quoting, consent, agent contact, agency registration/status, lead pulling, and market data. Missing lifecycle operations include updating an agency registration, managing credits/payments, and an explicit consent-revocation tool, since POST /forget is referenced but not exposed as an MCP tool.