Put a real Vedic astrology engine inside your AI

What the engine computes

Every result is a real, deterministic computation — not content keyed to a Sun sign. From an exact birth date, time and place, the engine (Swiss Ephemeris DE441, Lahiri ayanamsa, whole-sign houses by default) computes the sidereal chart and its full classical stack: planets, houses and lagna; 16 divisional (varga) charts; Vimshottari plus 21 conditional dasha systems; shadbala strength in rupas; ashtakavarga bindus; 100+ yogas; nakshatra structure; and live transits. Identical input yields byte-identical output, and every number is auditable. The engine only computes — your model does the interpretation.

The REST API

Two steps: compute a chart to get a chart_id and the list of section names, then read the sections you need (the full chart is large, so you fetch sections on demand).

# 1. Compute a chart
curl -s -X POST https://eternalevals.com/api/v1/chart \
  -H "Content-Type: application/json" \
  -d '{"date":"1990-07-07","time":"05:00","place":"Panipat, India"}'
# -> { "chart_id": "chart-abc123", "section_count": 71, "sections": [ ... ] }

# 2. Read a section (dot-paths work too: houses.10, planets.Saturn)
curl -s https://eternalevals.com/api/v1/chart/chart-abc123/section/vimshottari_dasha

Free with a per-IP rate limit. Add -H "Authorization: Bearer <key>" for a higher limit. The full machine-readable contract is at /openapi.json; a map of the whole site for AI crawlers is at /llms.txt.

MCP server for Claude & Cursor

The Model Context Protocol (MCP) lets assistants like Claude Desktop, Cursor and Cline call external tools. Our MCP server wraps the API as three tools — compute_chart, read_section, list_sections — so the assistant computes and reads a real Vedic chart natively, no copy-paste.

# Get the server + its dependencies
curl -O https://eternalevals.com/mcp/eternalevals_mcp.py
pip install mcp httpx

Then point your client at it. For Claude Desktop, add this to claude_desktop_config.json (Settings → Developer → Edit Config):

{
  "mcpServers": {
    "eternal-evals": {
      "command": "python",
      "args": ["/absolute/path/to/eternalevals_mcp.py"],
      "env": { "ETERNALEVALS_API_KEY": "" }
    }
  }
}

Cursor and Cline use the same command and env in their MCP settings. The API key is optional — leave it blank to use the free anonymous limit, or set a bearer key for a higher one. Restart the client and you'll see the three tools appear.

Why compute, not content

Ask a generic astrology chatbot about your Saturn and it may invent a placement that was never calculated — confident prose over unstable facts. Eternal Evals separates the two jobs: a deterministic engine computes the chart first, and the AI reads what the engine produced. That is the difference between a tool that talks about astrology and one that answers about your chart — and it is why an AI grounded in this engine can show the exact placement, strength or period behind any claim.

Frequently asked