Microburbs Australian Property Data

Chance of a sale within two years (by suburb)

suburbs_mesh_blocks_sale_probability

Read-onlyIdempotent

Modelled chance that a home in a suburb or city block sells within the next two years, as a percentage, for every block and suburb in Australia we score. Modelled estimates.

Coverage: About 262,000 mesh blocks and 12,100 suburbs across every state and territory; blocks we did not score return no data. Current to 2026-05-22. Full description, field list and method: /v1/datasets/sale-probability.

One call returns every mesh block in the suburb.

Price: 20¢ per call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "suburb_name": "<suburb_name>"
    },
    "name": "suburbs_mesh_blocks_sale_probability"
  }
}

Input Schema

Name	Required	Description	Default
`suburb_name`	Yes	Suburb name exactly as /v1/suburbs/list returns it, e.g. `Bondi` or `St Kilda (Vic.)`.

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 13, 2026

TDQS

A3.9/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (which already declare readOnly/idempotent/non-destructive), the description discloses the things an agent would otherwise have to discover: the values are modelled estimates, the dataset is current to 2026-05-22, unscored blocks return no data, one call returns all blocks in the suburb, and the cost is 20¢ per call. Cost and missing-data semantics are exactly the behavioral facts most definitions omit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded and the follow-on block (coverage, cardinality, price) is scannable. Minor redundancy: "every block and suburb in Australia we score" is restated as "blocks we did not score return no data," which costs a sentence but still clarifies edge-case behavior.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

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 full annotation coverage, the description supplies nearly everything needed to invoke it correctly, including pricing and the pointer to `/v1/datasets/sale-probability` for fields and method. The only real gap is that return fields are not enumerated and there is no output schema, though the external reference mitigates this.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the schema already specifies the exact-name format, an example, and the source list endpoint, so the baseline is 3. The description's only additive parameter fact is that the suburb name selects a whole group of mesh blocks, which clarifies scope but not the argument syntax.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence names a specific metric and unit ("Modelled chance that a home ... sells within the next two years, as a percentage") plus the geography granularity, so an agent knows exactly what it retrieves. It does not explicitly contrast with the similarly named sibling `suburbs_market_sale_probability`, so the agent must infer the difference from the phrase "One call returns every mesh block in the suburb."

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Useful scope cues are present: the tool covers only scored blocks in Australia, unscored blocks return no data, and each call returns all mesh blocks for one suburb. However, there is no explicit when-to-use/when-not guidance and no naming of the nearest alternative (`suburbs_market_sale_probability`), leaving the choice implied rather than stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Microburbs Australian Property Data tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?