Get consent
get_consentWhether a number is opted in or out, per registration, with its full consent history and proof.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 number |
get_consentWhether a number is opted in or out, per registration, with its full consent history and proof.
| Name | Required | Description | Default |
|---|---|---|---|
| phone | Yes | E.164 number |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive behavior, so the safety profile is covered. The description adds that results include full consent history and proof (useful return context), but says nothing about auth requirements, behavior for unregistered or unknown numbers, or rate limits.
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 front-loaded sentence with no filler; the core answer (opt-in/out) comes first and the enrichments (history, proof) follow.
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 one-parameter read tool with rich annotations and no output schema, this is nearly sufficient: it describes both the primary answer and the accompanying history/proof payload. Only edge-case behavior (unknown number, no registration) is left unstated.
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?
With a single parameter at 100% schema coverage ('E.164 number'), the schema already carries the burden, so baseline 3 applies. The phrase 'per registration' adds a small amount of semantic nuance about result scoping, but the description does not elaborate on the phone parameter itself.
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?
States a specific resource and scope: consent status (opted in/out) for a number, per registration, including history and proof. That is enough to distinguish it from set_consent (a write) and list_opt_outs (a listing), though it never names those siblings explicitly.
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?
Usage is only implied by the purpose statement — an agent can infer 'check one number's consent status before messaging.' There is no explicit when-to-use, when-not, or pointer to alternatives such as set_consent, export_consent, or list_opt_outs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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