Returns the clinic's Google review count and average rating, sourced from the live Google Business Profile.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | ||
| meta | Yes | ||
| evidence | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "data": {
+ "properties": {
+ "rating": {
+ "type": "number"
+ },
+ "source_url": {
+ "type": "string"
+ },
+ "user_ratings_total": {
+ "type": "number"
+ }
+ },
+ "required": [
+ "rating",
+ "source_url",
+ "user_ratings_total"
+ ],
+ "type": "object"
+ },
+ "evidence": {
+ "items": {
+ "properties": {
+ "checked_at": {
+ "type": "string"
+ },
+ "source": {
+ "type": "string"
+ },
+ "url": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "source",
+ "url",
+ "checked_at"
+ ],
+ "type": "object"
+ },
+ "type": "array"
+ },
+ "meta": {
+ "properties": {
+ "canonical_url": {
+ "type": "string"
+ },
+ "entity_id": {
+ "type": "string"
+ },
+ "last_updated": {
+ "type": "string"
+ },
+ "source_status": {
+ "enum": [
+ "verified",
+ "published"
+ ],
+ "type": "string"
+ }
+ },
+ "required": [
+ "entity_id",
+ "canonical_url",
+ "source_status"
+ ],
+ "type": "object"
+ }
+ },
+ "required": [
+ "data",
+ "meta",
+ "evidence"
+ ],
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds that the data is 'sourced from the live Google Business Profile', indicating it reflects current live data. No contradictions with annotations, and the addition of the live source is meaningful context beyond the structured metadata.
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 sentence with no fluff, front-loads the action ('Returns'), and includes only essential information about the resource and source. Every word earns its place.
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?
Given the tool has no parameters, an output schema is present to define return values, and the description covers the key purpose and data source. It is fully complete for a simple read-only getter, and no further elaboration is needed.
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 the description need not explain parameter semantics. The baseline for no parameters is 4, and the description adds no unnecessary parameter details.
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 clearly states the tool returns the clinic's Google review count and average rating, using a specific verb ('Returns') and resource. It is distinct from sibling tools that cover different information like clinic profile, treatments, or contact details.
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 provides clear context that the tool is for retrieving review count and rating, implying when to use it. However, it does not explicitly mention alternatives or cases where another sibling tool might be more appropriate, so it lacks explicit exclusions.
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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