Ephemeris MCP Server

Ephemeris MCP server: time-series foundation model (TSFM) forecasting for AI agents

Give Claude, Cursor, ChatGPT or any MCP client the ability to forecast numeric time series with prediction intervals: sales, demand, inventory, web traffic, signups, revenue, energy load, prices, sensor readings, infrastructure metrics.

Ephemeris runs a panel of open-weights, zero-shot time-series foundation models (TSFMs) behind one API key, plus an accuracy-weighted ensemble of them:

Send history, get quantile forecasts back. No training, no feature engineering, no GPU. Name a model, let Ephemeris route to the best fit for your data, or use the ensemble, an accuracy-weighted blend of the panel:

• TIME: level with the top of the leaderboard (MASE 0.639 vs 0.638 for the leader), with the best average MASE rank of 31 models
• GIFT-Eval: CRPS 0.4662 against seasonal naive, better than every model it blends (a few leaderboard entries, including TimesFM-3, score better)

Scored with each benchmark's own harness. Details: ephemeris.cascade.industries/benchmarks.

Tools

Get an API key

Sign up at ephemeris.cascade.industries, add credits, and create a key (pc_live_...) in the dashboard. Pay per forecast, no subscription: pricing.

Connect

Remote server (Streamable HTTP): https://ephemeris.cascade.industries/api/mcp, header Authorization: Bearer pc_live_...

Claude Code (plugin: MCP server plus a forecasting skill)

/plugin marketplace add TensorLink-AI/ephemeris-mcp
/plugin install ephemeris@ephemeris

You are asked for your API key once; it is stored in your system's secure credential store.

Claude Code (server only)

claude mcp add --transport http ephemeris https://ephemeris.cascade.industries/api/mcp \
  --header "Authorization: Bearer pc_live_your_key"

Cursor: one click with , then replace YOUR_EPHEMERIS_API_KEY with your key in Cursor's MCP settings. Or add it by hand:

Cursor (.cursor/mcp.json) and most clients

{
  "mcpServers": {
    "ephemeris": {
      "url": "https://ephemeris.cascade.industries/api/mcp",
      "headers": { "Authorization": "Bearer pc_live_your_key" }
    }
  }
}

VS Code (.vscode/mcp.json)

{
  "servers": {
    "ephemeris": {
      "type": "http",
      "url": "https://ephemeris.cascade.industries/api/mcp",
      "headers": { "Authorization": "Bearer pc_live_your_key" }
    }
  }
}

Claude Desktop and other clients that only run local (stdio) servers

{
  "mcpServers": {
    "ephemeris": {
      "command": "npx",
      "args": ["-y", "ephemeris-mcp"],
      "env": { "EPHEMERIS_API_KEY": "pc_live_your_key" }
    }
  }
}

OpenAI Responses API, Anthropic Messages API, Codex, Gemini CLI: see the docs.

Try it

Once connected, ask:

• "Here are my last 18 months of sales: … Forecast the next 6 months with an 80% interval."
• "Forecast next week's hourly traffic from this CSV and tell me the likely peak."
• "Use the ensemble to project daily signups for 90 days; plot the median and the 10th to 90th percentile band."

More in examples/prompts.md. Without MCP, the same forecast is one REST call: examples/rest_forecast.py.

Guides

• How to give Claude, Cursor or any AI agent a forecasting tool: setup for each client
• Forecasting in LangChain, the OpenAI Agents SDK and the Claude API
• Why language models are bad at forecasting numbers, and the split that works
• Which time-series foundation model should I use?
• Chronos-2 vs TimesFM 2.5 vs Toto 2 vs TiRex-2
• Tutorials: store sales, electricity load and solar, ops capacity, sensors and IoT
• All guides

Reference

• Full reference for LLMs: llms-full.txt
• API docs: ephemeris.cascade.industries/docs
• OpenAPI: openapi-m1.json
• Status: ephemeris.cascade.industries/status

The code in this repository (the plugin manifest, skill and stdio bridge) is MIT-licensed. The models keep their own licences, listed on each model page.