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Microburbs Australian Property Data

by au.com.microburbs
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Suburb · Climate normals

suburbs_environment_climate_normals
Read-onlyIdempotent

Average (2004-2020) climate for every suburb: daytime highs and overnight lows (annual, hottest and coldest month, every month), rainfall (annual, seasonal, monthly) and rain days, sunshine energy and afternoon humidity. Modelled estimates.

Coverage: About 15,300 suburbs and localities with a 5 km land grid cell within about 10 km of the suburb point; averaging period 2004-2020 (solar and humidity 2006-2020). Current to 2020-12-31. Full description, field list and method: /v1/datasets/climate-normals.

Price: 5¢ 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_environment_climate_normals"
  }
}

Input Schema

NameRequiredDescriptionDefault
toNoLast period to include, same formats as `from`.
fromNoFirst period to include: 2020, 2020-Q3, 2020-07 or 2020-07-01.
fieldsNoComma-separated subset of: temp_max_annual_c, temp_min_annual_c, temp_mean_annual_c, temp_max_hottest_month_c, temp_min_coldest_month_c, diurnal_range_c, rain_annual_mm, rain_days_annual, rain_wettest_month_mm, rain_driest_month_mm, rain_summer_mm, rain_autumn_mm, rain_winter_mm, rain_spring_mm, solar_exposure_mj_m2_day, solar_exposure_summer_mj_m2_day, solar_exposure_winter_mj_m2_day, humidity_afternoon_pct, humidity_afternoon_summer_pct, humidity_afternoon_winter_pct, temp_max_jan_c, temp_max_feb_c, temp_max_mar_c, temp_max_apr_c, temp_max_may_c, temp_max_jun_c, temp_max_jul_c, temp_max_aug_c, temp_max_sep_c, temp_max_oct_c, temp_max_nov_c, temp_max_dec_c, temp_min_jan_c, temp_min_feb_c, temp_min_mar_c, temp_min_apr_c, temp_min_may_c, temp_min_jun_c, temp_min_jul_c, temp_min_aug_c, temp_min_sep_c, temp_min_oct_c, temp_min_nov_c, temp_min_dec_c, rain_jan_mm, rain_feb_mm, rain_mar_mm, rain_apr_mm, rain_may_mm, rain_jun_mm, rain_jul_mm, rain_aug_mm, rain_sep_mm, rain_oct_mm, rain_nov_mm, rain_dec_mm. Omit for all.
suburb_nameYesSuburb name exactly as /v1/suburbs/list returns it, e.g. `Bondi` or `St Kilda (Vic.)`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety needs no restating. The description adds genuinely non-obvious context: the values are modelled estimates (a quality caveat), the spatial averaging method, the 2004-2020 baseline with solar/humidity at 2006-2020, currency to 2020-12-31, and a 5¢ per-call cost. It does not describe response shape or pagination, keeping it below a 5.

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?

Front-loads the payload (what the values are) before coverage caveats and pricing, with clear paragraph breaks and a pointer to the full spec. The measure list partially duplicates the schema's `fields` enumeration, which is mild redundancy rather than bloat.

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 read-only lookup with no output schema, the definition supplies the key facts an agent needs: what is returned (via field families and the schema's enumerated field list), the modelled-estimate caveat, geographic/time coverage, currency date, and cost. Only return formatting and error/empty-result behavior remain unstated.

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%, including the full enumerated field list and the accepted `from`/`to` formats, so the schema already carries parameter meaning. The description only restates the measure categories that map to `fields` and gives no extra syntax or default behavior beyond what the schema provides. Baseline 3 applies.

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?

States a specific resource and scope: average 2004-2020 climate normals per suburb, enumerating the measure families (temperature highs/lows, rainfall and rain days, solar exposure, afternoon humidity). An agent can tell this is a suburb-level climate-normals lookup rather than a heat-extremes or noise tool. It stops short of naming a distinguishing sibling, so it is clear but not sibling-differentiated.

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?

Usage is implied by the coverage statement (suburbs with a 5 km grid cell within ~10 km of the suburb point) and the pointer to /v1/datasets/climate-normals for method detail, which implicitly tells the agent when the data is valid. There is no explicit when-to-use/when-not guidance and no named alternative among the many environment siblings, so an agent must infer selection.

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 toolsInspect
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