How an out-of-state referral works
explain_referralExplains the referral process and the fee mechanics: who pays, who may be paid, and what it costs the consumer.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
explain_referralExplains the referral process and the fee mechanics: who pays, who may be paid, and what it costs the consumer.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "additionalProperties": false,
- "properties": {
- "consumer_cost": {
- "type": "string"
- },
- "fee_paid_by": {
- "type": "string"
- },
- "fee_paid_to": {
- "type": "string"
- },
- "outcome_guaranteed": {
- "type": "boolean"
- },
- "settlement_service_referral": {
- "type": "boolean"
- },
- "statute": {
- "type": "string"
- },
- "steps": {
- "items": {
- "type": "string"
- },
- "type": "array"
- }
- },
- "required": [
- "consumer_cost",
- "steps",
- "fee_paid_by",
- "fee_paid_to"
- ],
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool read-only, idempotent, and non-destructive, and the description is fully consistent with those. The description adds useful behavioral context by specifying exactly what the explanation will cover—who pays, who may be paid, and consumer cost—which is the expected content of the returned explanation.
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, front-loaded sentence. The colon-delimited list packs in meaningful detail without padding, and every phrase adds value for selecting and invoking the tool.
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 fully sufficient. It states the scope of the explanation clearly, annotations cover safety and side-effect behavior, and there is no output schema requirement because the content of the explanation is described directly.
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 parameter semantics are not relevant. Per the rubric, a zero-parameter tool receives a baseline of 4; the description correctly focuses on the tool's explanatory content rather than nonexistent inputs.
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 ('Explains') and identifies a clear resource ('the referral process and the fee mechanics'). It then enumerates the exact covered topics, making it easy to distinguish from sibling tools that get, list, or search rather than explain.
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 implies when to use this tool: whenever the referral process or its fee mechanics need to be explained. It does not explicitly name alternatives or exclusions, but no sibling tool plausibly competes for this explanatory role, so the context is clear.
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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