---

name: imf description: "IMF provides global macroeconomic data through the World Economic Outlook database, including historical statistics and forecasts for GDP growth, inflation, government debt, unemployment, trade balances, and other indicators across 190+ countries and regions, plus COFER reserve currency composition data."

IMF

Use this skill to answer questions that require IMF macroeconomic data, World Economic Outlook data, or COFER reserve currency composition data.

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/imf_tool.py describe from the plugin directory to call get_data_source_desc({"name": "imf"}).
2. Read the returned Markdown carefully. It contains the overall data source rules, country and region formats, indicator names, dataset 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, year or date range, forecast versus historical periods, dataset or source table, unit, and frequency requirements.
5. Use python3 scripts/imf_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

• Cross-country comparison of GDP growth, inflation, unemployment, government debt, current account, trade balance, or other macroeconomic indicators.
• Historical macroeconomic trend analysis and World Economic Outlook forecasts.
• Policy research that requires IMF country, region, or global macro data.
• COFER analysis of official foreign exchange reserve currency shares such as USD, EUR, CNY, JPY, GBP, CHF, AUD, and CAD.

Script

Use the bundled script from the plugin directory:

python3 scripts/imf_tool.py describe

After reading the Markdown and selecting an API:

python3 scripts/imf_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": "imf"} to get_data_source_desc
• sends {"data_source_name": "imf", "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.