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 safety profile is covered. The description adds value beyond annotations by disclosing that the return is machine-readable and by describing the content categories the agent will receive (retention, revocation, consent text). No contradiction with annotations.
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 sentences carry substantial information with no filler: the first enumerates the five content aspects, the second adds the machine-readable affordance and its purpose. Slightly dense as a run-on opening sentence, but every clause earns its place and the key question is front-loaded.
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 is largely complete: it states what the tool returns, why it is machine-readable, and when to fetch it (before request_agent_contact). Minor gap: it does not describe the exact output format or field structure, but the content enumeration largely compensates.
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 the baseline is 4. The description compensates by explaining what the returned artifact contains (data handling terms, consent wording), which is the only semantic information an agent needs for a parameterless lookup.
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 opens with a concrete question ('What happens to anything you send us') and enumerates exactly what the tool provides: recipient, purpose, retention, revocation, and consent wording. This clearly identifies the resource (data use/consent terms) and distinguishes it from siblings like request_agent_contact, agency_status, or get_quotes.
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 gives explicit timing guidance: the consent wording should be presented 'before request_agent_contact', and the data is for evaluating 'the exchange before making it'. It names the relevant sibling and anchors usage to that workflow. However, it does not state when to avoid this tool or name exclusion cases, so it falls just short of a 5.
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 function: eligibility checking, quoting, agent contact, agency registration/status/queue, data terms, and market data. The only adjacent pair, agency_status and pull_requests, is cleanly separated by status versus queue draining.
Five names use an imperative verb_noun pattern (check_eligibility, get_quotes, register_agency, pull_requests, request_agent_contact), while agency_status, data_use_terms, and market_data are resource-style names. All are lowercase underscore names and still readable, so this is a minor deviation rather than a chaotic mix.
Eight tools is well scoped for a platform covering eligibility, quotes, consumer contact, agency onboarding/status/queue, data terms, and market data. Each tool has a distinct job and none feels redundant.
The core journey is covered end to end: check eligibility, get quotes, request agent contact, register an agency, check status, and pull records. Minor gaps exist around agency profile updates/removal and exposing a revoke/forget action as a first-class tool, but agents can complete the main workflows without dead ends.