Ephemeris Time-Series Forecasting

Probabilistic time-series forecasts from zero-shot foundation models: routed, single or ensembled.

What does this entry provide?

Ephemeris Time-Series Forecasting is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Ephemeris Time-Series Forecasting to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: Official MCP Registry
Quality: 78/100
Install method: streamable-http
Repo: TensorLink-AI/ephemeris-mcp
Repo updated: Oct 6, 2026

Install command

mcp add industries.cascade/ephemeris https://ephemeris.cascade.industries/api/mcp

Install method: streamable-http

Review the upstream source before running this command.

GitHub project Homepage

Quality

Repo stars

Repo updated

Oct 6, 2026

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

MCP manifest

{
  "_meta": {
    "io.modelcontextprotocol.registry/official": {
      "status": "active",
      "isLatest": true,
      "updatedAt": "2026-10-06T04:15:08.712587Z",
      "publishedAt": "2026-10-06T04:15:08.712587Z",
      "statusChangedAt": "2026-10-06T04:15:08.712587Z"
    }
  },
  "server": {
    "name": "industries.cascade/ephemeris",
    "icons": [
      {
        "src": "https://raw.githubusercontent.com/TensorLink-AI/ephemeris-mcp/main/assets/logo-256.png",
        "sizes": [
          "256x256"
        ],
        "mimeType": "image/png"
      },
      {
        "src": "https://raw.githubusercontent.com/TensorLink-AI/ephemeris-mcp/main/assets/logo-512.png",
        "sizes": [
          "512x512"
        ],
        "mimeType": "image/png"
      }
    ],
    "title": "Ephemeris Time-Series Forecasting",
    "$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
    "remotes": [
      {
        "url": "https://ephemeris.cascade.industries/api/mcp",
        "type": "streamable-http",
        "headers": [
          {
            "name": "Authorization",
            "isSecret": true,
            "isRequired": true,
            "description": "Bearer token: \"Bearer pc_live_...\". Create an API key at https://ephemeris.cascade.industries/dashboard after signing up."
          }
        ]
      }
    ],
    "version": "1.0.1",
    "packages": [
      {
        "version": "1.0.0",
        "transport": {
          "type": "stdio"
        },
        "identifier": "ephemeris-mcp",
        "registryType": "npm",
        "environmentVariables": [
          {
            "name": "EPHEMERIS_API_KEY",
            "isSecret": true,
            "isRequired": true,
            "description": "Ephemeris API key (pc_live_...), from https://ephemeris.cascade.industries/dashboard/api-keys"
          }
        ]
      }
    ],
    "repository": {
      "url": "https://github.com/TensorLink-AI/ephemeris-mcp",
      "source": "github"
    },
    "websiteUrl": "https://ephemeris.cascade.industries",
    "description": "Probabilistic time-series forecasts from zero-shot foundation models: routed, single or ensembled."
  }
}

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