Vedic astrology computation
reimagined from first principles.
KundaliMCP is the first agentic-AI-native Jyotish computation platform — a Model Context Protocol toolkit that gives AI agents the ability to cast, analyze, and interpret kundali charts with full deterministic transparency. Not a chatbot wrapper. Not a lookup table. A computational engine that models the full complexity of Jyotish as a graph problem.
A computation engine, not a chatbot.
Existing Jyotish software treats chart computation as a solved problem and interpretation as a black box. KundaliMCP treats both as engineering problems. Computation is a graph traversal, not a lookup table. Interpretation is deterministic rule application, not LLM hallucination.
Every conclusion has a provenance chain back to the astronomical facts. Every rule carries its school attribution — no silent bias. The same input always produces the same output from the deterministic layer; the interpretive layer is tunable but never touches the astronomical foundation.
The technologies of 2026 — graph databases, defeasible reasoning engines, MCP protocol, multi-pass analysis on commodity hardware — make it feasible to model Jyotish the way it was designed to work: as a recursive, multi-dimensional system where every factor affects every other factor.
Agents call compute_chart, explain, forecast_events — and get back fully traceable, multi-school, bilingual results grounded in classical texts.
Why KundaliMCP is different.
Ephemeris to product. All in-house.
No third-party ephemeris. No vendor lock. The entire stack from raw sky coordinates to MCP response is owned and auditable. A new primitive flows from sky to screen in days.
Every number is reproducible.
The audit harness at audit/life_events/ validates against BPHS, Sāravalī, Phaladīpikā, Jaimini Sūtras, and JPL DE421. Run it yourself: cargo run --release --bin audit-life-events
Every rule cited. No silent assumptions.
Every life-event trigger, yoga definition, and dasha rule in the engine is derived from a named classical source at chapter:verse level — not from a prior-generation reference and not from convention. If a source contradicts another, both are recorded with their respective school attributions.
Built for builders.
AI agent builders
Add classical Jyotish computation to any MCP-compatible agent — Claude, ChatGPT, LangChain, CrewAI, AutoGen, or your own. The protocol is the product; no adapter layer, no SDK, no glue code.
Jyotish software developers
Use KundaliMCP as a computation backend. 18 tools, all five dasha systems, all 16 divisional charts, 7 languages — production-ready today.
Researchers
Study traditional knowledge systems with modern computational tools. Every rule is cited; every result is reproducible; the audit harness is public.
Practitioners
Transparent, multi-school analysis. See the provenance chain behind every interpretation. No black boxes. No silent school blending.
ArthIQ Labs
Human intent, meets AI-native execution.
ArthIQ Labs is an AI-native incubation studio, built on agentic engineering. Coding agents handle first-pass execution across the software lifecycle; humans own architecture, tradeoffs, and outcomes.
The work of a quarter, in a week. Live products at roughly a tenth the cost of a traditional team. No concept decks, no endless pilots — concept to launch in 7–21 days.
Visit arthiq.net →Strategy, architecture, and roadmapping for AI-native businesses.
End-to-end design and shipping of AI-native products. Concept to launch in 7–21 days.
Operations, support, and agentic process automation for AI systems in production.
Ready to add Jyotish to your agent stack?
Get a key in 60 seconds. No credit card required on the free tier. 18 tools, 7 languages, 3 schools — live at mcp.kundalimcp.com/mcp.