forecast-mcp

forecast-mcp

Enable any AI agent to forecast time-series data (e.g., sales, traffic) using Google's TimesFM or a zero-dependency statistical baseline.

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README

timesfm-mcp

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MCP server for Google's TimesFM 2.5 — give any AI agent zero-config time-series forecasting.

Plug TimesFM 2.5, Google's 200M-parameter foundation model for time-series, directly into Claude Code, Claude Desktop, Cursor, or any MCP client. The agent calls forecast, gets point predictions + uncertainty bands + a trend/seasonality summary, and writes the explanation itself.

No ML configuration. No data pipelines. One line to run.

Forecast chart: 24-month MRR history with 6-month point forecast and 90% confidence band

Chart generated with the statistical baseline. Install the timesfm extra (pip install "timesfm-mcp[timesfm]") to use the full TimesFM 2.5 neural model.

Quickstart (30 seconds)

uvx timesfm-mcp        # runs over stdio for local agents

Add to your Claude Desktop / Claude Code / Cursor config:

{
  "mcpServers": {
    "forecast": { "command": "uvx", "args": ["timesfm-mcp"] }
  }
}

Then ask your agent: "Forecast the next 6 months from this revenue data and tell me what to expect."

Enable TimesFM 2.5

pip install "timesfm-mcp[timesfm]"

The server auto-detects TimesFM and uses it; otherwise it falls back to the built-in statistical baseline. Both backends always return a result — no configuration needed.

Two backends, zero config

Backend When active What it needs
TimesFM 2.5 (Google) When installed pip install "timesfm-mcp[timesfm]"
Statistical baseline Always Just NumPy — already a dependency

Tools

Tool What it does
forecast Forecast a single series with optional uncertainty bands
list_backends Report which engine is active (timesfm / baseline)
backtest Hold out the last N points — compare TimesFM vs baseline MAE/sMAPE

Documentation

Full docs in the docs/ folder:

Migrating from forecast-mcp

timesfm-mcp is the renamed continuation of forecast-mcp. Update your install:

pip install timesfm-mcp      # replaces: pip install forecast-mcp
uvx timesfm-mcp              # replaces: uvx forecast-mcp

Update your agent config: change "args": ["forecast-mcp"] → "args": ["timesfm-mcp"].

License

Apache-2.0

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