get_generation_mix_timeline

Retrieve electricity generation breakdown by source over time to visualize energy transition patterns and analyze grid composition.

Instructions

Get generation mix over time for a full day or period.

Returns generation breakdown by source across multiple time points, useful for visualizing energy transition patterns.

Args: date: Date in YYYY-MM-DD format time_granularity: Time aggregation (hour or day, default: hour)

Returns: JSON string with generation mix timeline.

Examples: Get hourly generation mix for a day: >>> await get_generation_mix_timeline("2025-10-08", "hour")

Get daily generation mix for a month:
>>> await get_generation_mix_timeline("2025-10-01", "day")

Input Schema

TableJSON Schema

Name	Required	Description	Default
`date`	Yes
`time_granularity`	No		hour

Implementation Reference

src/ree_mcp/interface/mcp_server.py:554-554 (registration)
MCP tool registration and handler entrypoint for get_generation_mix_timeline. Decorated with @mcp.tool(), parses input, instantiates GenerationMixService, calls its method, and formats response.
```
@mcp.tool()
```

src/ree_mcp/interface/tool_services.py:168-227 (handler)

Core handler logic in GenerationMixService.get_generation_mix_timeline. Fetches data for multiple generation sources using DataFetcher, aligns timestamps, computes totals per time point, and builds timeline response.

async def get_generation_mix_timeline(
    self, start_date: str, end_date: str, time_granularity: str = "hour"
) -> dict[str, Any]:
    """Get generation mix over a period.

    Args:
        start_date: Start datetime in ISO format
        end_date: End datetime in ISO format
        time_granularity: Time aggregation level

    Returns:
        Timeline data with generation mix at each point
    """
    sources = IndicatorIDs.get_generation_mix_sources()
    raw_data = await self.data_fetcher.fetch_multiple_indicators(
        sources, start_date, end_date, time_granularity
    )

    result: dict[str, Any] = {
        "period": {
            "start": start_date,
            "end": end_date,
            "granularity": time_granularity,
        },
        "timeline": [],
    }

    # Build timeline by combining data points
    source_data: dict[str, list[dict[str, Any]]] = {}
    for source_name, response_data in raw_data.items():
        if "error" not in response_data:
            source_data[source_name] = response_data.get("values", [])
        else:
            source_data[source_name] = []

    if source_data:
        # Use first source to get timestamps
        first_source_values = next(iter(source_data.values()))
        for i, value_point in enumerate(first_source_values):
            timestamp = value_point["datetime"]

            generation_point: dict[str, Any] = {
                "datetime": timestamp,
                "sources": {},
                "total_mw": 0.0,
            }

            for source_name, values in source_data.items():
                if i < len(values):
                    mw_value = values[i]["value"]
                    generation_point["sources"][source_name] = mw_value
                    generation_point["total_mw"] += mw_value
                else:
                    generation_point["sources"][source_name] = 0.0

            generation_point["total_mw"] = round(generation_point["total_mw"], 2)
            result["timeline"].append(generation_point)

    return result

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