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Worldbank Climate

by io.github.pipeworx-io
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Get Climate Data

get_climate_data
Read-onlyIdempotent

World Bank CCKP climate data for any country: historical observations and CMIP6 climate-model projections. Returns mean temperature, max/min temperature, or precipitation, by emissions scenario. Pass the components as separate args — this tool assembles the brittle composite "indicator code" for you. Values are returned keyed by ISO3 country code; the inner key is "-07" (annual/period) or per-season months.

WORKED EXAMPLES (all verified live):

  1. Historical annual mean temperature for the USA (defaults): {"geography":"USA"} => tas climatology over 1995-2014, ~10.2°C.

  2. Projected warming (anomaly) for the USA mid-century under a high scenario: {"geography":"USA","scenario":"ssp585","period":"2040-2059","product":"anomaly"} => ~2.4°C above baseline.

  3. Historical annual precipitation for all countries: {"geography":"all_countries","variable":"pr","product":"climatology","period":"1995-2014","scenario":"historical"} => mm/year per country.

If assembly ever fails for an exotic combination, pass the full composite string via indicator_code instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
periodNoTime period. For historical use "1995-2014" (the default when scenario=historical). For projections use a 20-year window e.g. "2020-2039","2040-2059","2060-2079","2080-2099". Defaults to 1995-2014 (historical) or 2040-2059 (projection).
productNoWhat to return: "climatology" (absolute values, verified) or "anomaly" (change vs reference period — use with a projection scenario, verified). Default "climatology".
scenarioNoEmissions/scenario. "historical" for observed past, or a projection SSP: "ssp119","ssp126","ssp245","ssp370","ssp585" (low→high emissions). Default "historical".
variableNoClimate variable. Verified: "tas" (mean temp °C), "tasmax" (max temp), "tasmin" (min temp), "pr" (precipitation mm/year). Default "tas".
geographyNoISO3 country code (e.g. "USA", "BRA"), or "all_countries" for every country. Default "all_countries".
collectionNoDataset collection. Default and only fully API-verified value is "cmip6-x0.25" (model-derived, covers historical + projections). The portal lists "era5-x0.25"/"cru-x0.5" observation collections but those did not return data through this JSON API in testing — prefer cmip6-x0.25.
aggregationNo"annual" (single value) or "seasonal" (four season values), verified. Default "annual".
indicator_codeNoOptional raw 11-token composite code to pass through verbatim, bypassing assembly. Order: collection_type_variable_product_aggregation_period_percentile_scenario_model_modelCalculation_statistic. Example: "cmip6-x0.25_climatology_tas_climatology_annual_1995-2014_median_historical_ensemble_all_mean".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "geography": "USA"
+  },
+  {
+    "geography": "USA",
+    "period": "2040-2059",
+    "product": "anomaly",
+    "scenario": "ssp585"
+  },
+  {
+    "geography": "all_countries",
+    "period": "1995-2014",
+    "product": "climatology",
+    "scenario": "historical",
+    "variable": "pr"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond annotations indicating readOnly and non-destructive behavior, the description reveals key behavioral traits: automatic indicator code assembly, return format (ISO3 keys, startYear-07), and failed API testing for era5/cru collections. This adds substantial value.

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?

The description is front-loaded with purpose and includes detailed worked examples. While somewhat long, the examples are verified and earn their place. Slight room for brevity, but well-structured.

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

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 8 parameters, no output schema, and no nested objects, the description provides comprehensive coverage: default values, return format, collection caveats, and fallback mechanism. It leaves little ambiguity for correct invocation.

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

Parameters4/5

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining composite indicator code construction, parameter defaults, and warnings (e.g., collection preference). This elevates the score above baseline.

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

Purpose5/5

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

The description explicitly states the tool retrieves World Bank CCKP climate data, listing data types (historical observations, projections), variables (temperature, precipitation), and the automatic assembly of indicator codes. It is specific and distinguishes from unrelated sibling tools.

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

Usage Guidelines4/5

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

The description includes worked examples demonstrating typical use cases and explains when to use the indicator_code fallback. It does not explicitly state when not to use the tool, but given no direct siblings for climate data, this is a minor gap.

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

All Worldbank Climate toolsInspect
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