suburbs_crime_summarySuburb and national total-crime medians (predicted incidents per 100,000 residents per year) with a vs-national verdict.
| Name | Required | Description | Default |
|---|---|---|---|
| suburb_name | 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": [
- {
- "additionalProperties": true,
- "description": "Suburb vs national total-crime benchmark medians.",
- "example": {
- "area_level": "suburb",
- "area_name": "Belmont North",
- "national_median": 2787.6,
- "suburb_median": 2248,
- "unit": "predicted incidents per 100,000 residents per year",
- "verdict": "Below average",
- "vs_national_pct": -19
- },
- "properties": {
- "area_level": {
- "description": "Always 'suburb' for these endpoints.",
- "title": "Area Level",
- "type": "string"
- },
- "area_name": {
- "description": "Suburb (SAL) name.",
- "title": "Area Name",
- "type": "string"
- },
- "national_median": {
- "description": "Median total crime rate across all Australian mesh blocks, same unit as suburb_median.",
- "title": "National Median",
- "type": "number"
- },
- "suburb_median": {
- "description": "Median total crime rate across this suburb's mesh blocks — predicted incidents per 100,000 residents per year (model trained on QLD Police 2020-2022 incident averages, predicted for all Australian mesh blocks).",
- "title": "Suburb Median",
- "type": "number"
- },
- "unit": {
- "description": "Unit of suburb_median and national_median — always 'predicted incidents per 100,000 residents per year'.",
- "title": "Unit",
- "type": "string"
- },
- "verdict": {
- "description": "Plain-English band for the delta: 'Very low', 'Below average', 'Average', 'Above average' or 'High'.",
- "title": "Verdict",
- "type": "string"
- },
- "vs_national_pct": {
- "description": "Suburb median vs national median, as a rounded percentage delta (negative = less crime than the national median).",
- "title": "Vs National Pct",
- "type": "integer"
- }
- },
- "required": [
- "area_name",
- "area_level",
- "unit",
- "suburb_median",
- "national_median",
- "vs_national_pct",
- "verdict"
- ],
- "title": "CrimeSummary",
- "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[CrimeSummary]",
- "type": "object",
- "x-fastmcp-top-level-schema": "ApiResponse_CrimeSummary_"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds useful context about units (predicted incidents per 100,000) and the verdict aspect, which is beyond annotations. It doesn't discuss rate limits or auth, but with annotations covering the safety profile, this is adequate.
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, front-loaded with the core output (medians and verdict) and units. There is zero waste and every phrase adds information.
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 simple one-parameter tool with no output schema, the description conveys the essential data returned. However, it omits how the verdict is derived or what 'vs-national' entails, and it doesn't clarify the expected format of suburb_name. Given the abundance of similar siblings, the lack of usage guidance also leaves completeness slightly lacking.
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?
Schema description coverage is 0%, so the description must compensate. It does not mention the suburb_name parameter at all—no format, constraints, or examples. The parameter name is self-explanatory, but the description adds no semantic value beyond what the property name implies, failing to offset the low schema coverage.
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 states the tool provides suburb and national total-crime medians with a vs-national verdict. This is a specific resource and metric, and it distinguishes from siblings like suburbs_crime_breakdown (which likely breaks down by crime type) and suburbs_crime_by_mesh_block (mesh block level). It lacks an explicit verb like 'returns' but is clear enough.
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 no guidance on when to use this tool versus the many crime-related siblings. It doesn't mention alternatives, prerequisites, or exclusions. The title hints at the benchmark focus but the description itself provides no routing information.
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.