World Bank Open Data

World Bank Open Data

Use this skill to answer questions that require World Bank Open Data country indicators, development metrics, or national-level time series.

Setup

Check whether the agent-gw Python SDK is available in the current Python environment, and install it only if the check fails:

python3 -c "import agent_gw" || python3 -m pip install "$(curl -s https://cdn.kimi.com/agentgw/pysdk/manifest.json | python3 -c "import json,sys; print(json.load(sys.stdin)['latest']['url'])")"

The SDK needs an API key from api_key=..., KIMI_API_KEY, or ~/.kimi/agent-gw.json.

Workflow

1. Run python3 scripts/world_bank_open_data_tool.py describe from the plugin directory to call get_data_source_desc({"name": "world_bank_open_data"}).
2. Read the returned Markdown carefully. It contains the overall data source rules, country formats, indicator formats, date range constraints, and each API's description, required parameters, optional parameters, defaults, and allowed values.
3. Select the API that best matches the user's question.
4. Build params exactly from the Markdown requirements. Pay attention to country or region, indicator code or name, year range, unit, source, frequency, and national-level data constraints.
5. Use python3 scripts/world_bank_open_data_tool.py call to call call_data_source_tool.
6. If the call fails, explain the failure reason from the response.
7. If the call succeeds, save any returned files first, then answer using resp.result.assistant; ignore resp.result.user unless display content is specifically needed.

Common Use Cases

• Country-level time series for GDP, GNP, population, poverty rates, unemployment, trade, inflation, education, health, and environmental data.
• Cross-country comparison of development indicators.
• Long-run trend analysis using annual data from 1960 to present where available.
• Economic, social, and environmental research that needs World Bank indicator definitions and national-level observations.

Script

Use the bundled script from the plugin directory:

python3 scripts/world_bank_open_data_tool.py describe

After reading the Markdown and selecting an API:

python3 scripts/world_bank_open_data_tool.py call \
  --api-name "<api name from markdown>" \
  --params-json '{"required_param":"value"}'

For larger params, write a JSON object and pass --params-file path/to/params.json.

The script:

• sends {"name": "world_bank_open_data"} to get_data_source_desc
• sends {"data_source_name": "world_bank_open_data", "api_name": ..., "params": ...} to call_data_source_tool
• prints failure messages from error.user or error.assistant
• saves returned files to each files[].name path returned by the data source
• prints the joined result.assistant texts on success

Expected call_data_source_tool response shape:

{
    "is_success": bool,
    "result": {"user": list[str], "assistant": list[str]} | None,
    "error": {"user": list[str], "assistant": list[str]} | None,
    "files": [{"name": str, "content": str}],
}

When files are returned, name is the file path or name to write. The path is usually dictated by the selected API's params in the Markdown docs. If an API does not need files, the response normally has no files to save.