properties_valuation_avmMicroburbs ML AVM for a single GNAF — point estimate, 80% range, plus a confidence score (0–100) derived from the interval width.
Price: 10¢ per call.
| Name | Required | Description | Default |
|---|---|---|---|
| gnaf_id | Yes |
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "properties": {
- "available": {
- "anyOf": [
- {
- "type": "boolean"
- },
- {
- "type": "null"
- }
- ],
- "description": "`false` on no-data responses. Omitted on success — branch on `data !== null` if you want a single discriminator.",
- "title": "Available"
- },
- "data": {
- "anyOf": [
- {
- "description": "Automated valuation: point estimate + 80% prediction interval +\nconfidence score derived from the interval width.",
- "example": {
- "predicted_price": 876651,
- "predicted_price_high": 1183479,
- "predicted_price_low": 569823,
- "prediction_date": "2026-04-20"
- },
- "properties": {
- "confidence_score": {
- "anyOf": [
- {
- "type": "integer"
- },
- {
- "type": "null"
- }
- ],
- "description": "0–100. The valuation model's own calibrated confidence in this estimate — higher means the model is more certain. Read alongside `predicted_price_low` / `predicted_price_high` for the range.",
- "title": "Confidence Score"
- },
- "predicted_price": {
- "description": "Point estimate, AUD.",
- "title": "Predicted Price",
- "type": "number"
- },
- "predicted_price_high": {
- "description": "Upper bound of the 80% prediction interval, AUD.",
- "title": "Predicted Price High",
- "type": "number"
- },
- "predicted_price_low": {
- "description": "Lower bound of the 80% prediction interval, AUD.",
- "title": "Predicted Price Low",
- "type": "number"
- },
- "prediction_date": {
- "description": "Date the model produced this estimate. AVMs are refreshed in batches; not on demand.",
- "title": "Prediction Date",
- "type": "string"
- }
- },
- "required": [
- "predicted_price",
- "predicted_price_low",
- "predicted_price_high",
- "prediction_date"
- ],
- "title": "AvmEstimate",
- "type": "object"
- },
- {
- "type": "null"
- }
- ],
- "description": "The endpoint's payload, or `null` when Microburbs has no value."
- },
- "message": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "description": "Human-readable explanation. Omitted on success.",
- "title": "Message"
- },
- "reason": {
- "anyOf": [
- {
- "type": "string"
- },
- {
- "type": "null"
- }
- ],
- "description": "Machine-readable slug naming the no-data condition (e.g. `no_avm_for_GANSW704074813`). Stable per endpoint. Omitted on success.",
- "title": "Reason"
- }
- },
- "title": "ApiResponse[AvmEstimate]",
- "type": "object",
- "x-fastmcp-top-level-schema": "ApiResponse_AvmEstimate_"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, covering safety. The description adds valuable context: the cost (10¢ per call) and the nature of the output (point estimate, range, confidence score). This goes beyond the annotations without contradicting them.
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 extremely concise: one sentence plus a bolded price note. It front-loads the core purpose and includes the price as a separate line, making it easy to scan. Every element adds value with no redundancy.
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?
The description provides the key output elements (point estimate, range, confidence score) but omits details like units (presumably currency), how to interpret the range or confidence, and any error handling or edge cases. Since there is no output schema, more explicit return structure would be helpful.
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 schema has no description for the single parameter gnaf_id (0% coverage). The description only says 'for a single GNAF' but does not explain what a GNAF is, its format, or how to obtain it. This is insufficient for an agent to correctly construct the parameter without prior knowledge.
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's function: providing an automated valuation (AVM) for a single property identified by a GNAF ID, returning a point estimate, an 80% range, and a confidence score. It distinguishes itself from other valuation tools (e.g., agent_quoting, land_values) by being the ML-based AVM, though it doesn't explicitly contrast with them.
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?
No guidance is given on when to use this tool versus the many sibling valuation tools (e.g., properties_valuation_agent_quoting, properties_valuation_value_series). The agent must infer from the name and description alone, with no explicit conditions or alternatives mentioned.
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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