timesfm-mcp 0.1.0

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

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.

Illustrative — statistical baseline backend. Install the timesfm extra to use the full TimesFM 2.5 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