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.
README
timesfm-mcp
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.

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:
- Getting Started — installation and first forecast
- Client Setup — Claude Desktop, Claude Code, Cursor configs
- Tool Reference — full parameter docs
- Cookbook — SaaS MRR, e-commerce demand, traffic, cloud spend
- How It Works — the math and model
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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