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Vedaksha

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Vedic astrology and astronomical ephemeris: kundali, panchanga, dashas, vargas, transits

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "vedaksha": {
      "command": "npx",
      "args": [
        "-y",
        "vedaksha"
      ]
    }
  }
}

πŸ’‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Directory Badge Claim listing AlternativesπŸ’» More in Developer Tools

Documentation Overview

Vedaksha β€” Vision from Vedas

Clean-room Rust ephemeris and Vedic astrology engine, built for the agentic-AI era. Sub-arcsecond planetary precision, every algorithm traced to a primary source, any chart queryable as a property graph.

crates.io docs.rs PyPI npm CI MSRV License

Website Β· Docs Β· Playground Β· API reference Β· Blog

clean-room Β· 0.103β€³ mean longitude vs JPL Horizons Β· 1,147 tests (per push) + 24,350 oracle rows Β· MCP-native Β· BUSL-1.1 β†’ Apache 2.0

Install Β· Quick start Β· Accuracy Β· What's inside Β· MCP + property graph Β· Provenance Β· License


Install

PlatformInstallNotes
Rustcargo add vedakshafull pipeline
Pythonpip install vedakshaengine via WebAssembly, py3-none-any, Python β‰₯ 3.9 β€” no Rust toolchain
WASMnpm install vedaksha-wasmbrowser & edge, no data files
MCPcargo install vedaksha-mcpstdio + HTTP (bearer auth)
Dockerdocker run -e VEDAKSHA_MCP_TOKEN=… -p 3100:3100 ghcr.io/arthiqlabs/vedaksha-mcpmulti-arch (amd64 + arm64)

Compute janam kundali (natal charts), panchanga, dashas, nakshatras, vargas, shadbala, ashtakavarga, muhurta and transits/gochara from a sub-arcsecond ephemeris (VSOP87A, ELP/MPP02, JPL DE440s/DE441).

mermaid
flowchart LR
  K["JPL DE440s / DE441<br/>SPK kernel, ~31 MB"] --> E
  V["VSOP87A + ELP/MPP02<br/>compiled to constants,<br/>no data files"] --> E
  E["Ephemeris core<br/>precession Β· nutation<br/>light-time Β· Ξ”T"] --> W["Western<br/>houses Β· aspects<br/>dignities"]
  E --> J["Jyotish<br/>nakshatras Β· dashas Β· vargas<br/>panchanga Β· shadbala Β· muhurta"]
  W --> C["ComputedChart"]
  J --> C
  C --> G["Property graph<br/>Cypher Β· SurrealQL<br/>JSON-LD Β· RAG text"]
  C --> M["MCP server<br/>17 tools Β· stdio + HTTP"]
  C --> S["Rust Β· Python Β· WASM"]

Quick start

server.ts
from vedaksha import Vedaksha

vk = Vedaksha()
chart = vk.natal_chart(julian_day=2451545.0, latitude=28.6139, longitude=77.2090)
bash
cargo install vedaksha-mcp && vedaksha-mcp     # stdio: Claude Desktop, Cursor, VS Code

The Rust path is a compiled doctest in crates/vedaksha/src/lib.rs.

Every Julian Day on the public surfaces is UT1, not TT and not TDB. The engine converts to TT internally for the dynamical terms and uses UT1 for Earth rotation, which fixes the ascendant, MC and all twelve cusps. Passing a TDB Julian Day adds Ξ”T worth of rotation instead of removing it β€” 0.289Β° (17.3β€²) at today's Ξ”T β‰ˆ 69 s, on every cusp. The one exception is the raw SPK query (state_vector), which indexes the kernel directly and takes TDB.

Accuracy

These figures are reproducible, not just asserted: vedaksha-parity is a separate harness (maintained by the same authors as this engine) that anyone can run against their own copies of the reference kernels to check the published numbers below.

Every figure is printed by a named test. Reproduce the ephemeris tables with bash scripts/download_de440s.sh, then cargo test -p vedaksha-ephem-core --release -- --include-ignored --nocapture; the ayanamsha figures come from cargo test -p vedaksha-astro sidereal, and the cross-check against an independent Python derivation of the same primaries from cargo test -p vedaksha-astro --test ayanamsha_fixture.

What the headline 0.103β€³ measures β€” precisely. It is a mean apparent geocentric ecliptic-longitude residual, not a general position-accuracy figure: the fixture also carries ref_latitude, ref_distance and ref_speed, but oracle_comparison.rs marks them dead_code and none of the three enters this number. It covers only the measured-Ξ”T era, 1900–2025, at 1,535 comparisons per body (15,350 total across 10 bodies) β€” the full 1900–2100 grid moves the mean to 0.878β€³ for reasons unrelated to ephemeris accuracy (see below). And it compares against planetary-system barycentres, not physical planet centres, matching what the DE440s kernel stores and what SpkReader returns; querying centres instead would inject a spurious ~0.1β€³ offset for the outer planets (scripts/generate_horizons_oracle.py) β€” the same order of magnitude as the headline mean itself. These three facts are also published as data in metrics.json's accuracy block (quantity, era, targetConvention), so the site renders them rather than anyone hand-copying this paragraph.

SpkReader vs JPL Horizons (DE441) β€” oracle_comparison.rs, 24,350 committed rows (10 bodies Γ— 2,435 dates, 1900–2100). Horizons serves DE441, so this measures our DE440s pipeline against an independent kernel.

EraComparisonsMeanMax
1900–2025 (Ξ”T measured)15,3500.103β€³1.187β€³ (Uranus)
1900–2100 (all)24,3500.878β€³44.912β€³ (Moon, 2099)

15,349 of 15,350 comparisons before 2026 are sub-arcsecond. Past 2025 the residual is Ξ”T prediction, not ephemeris error: our Espenak–Meeus extrapolation and Horizons' Ξ”T diverge by ~68 s at 2099, and the error scales with a body's angular rate β€” the Moon (0.64β€³/s) picks up ~45β€³, Pluto essentially none. At 2099-02-06, five bodies spanning 0.03–0.64β€³/s all imply the same 66–71 s offset, which is the signature of a clock difference, not a position error.

AnalyticalProvider vs JPL Horizons β€” analytical_oracle.rs, 1900–2025: overall mean 0.239β€³, worst case 1.896β€³ (Neptune), Moon 0.169β€³ mean via ELP/MPP02. 13,815 comparisons across 9 bodies (1,535 dates per body) over the measured-Ξ”T era; see metrics.json's accuracy.analytical block.

Per body, because the mean hides a pattern worth knowing before you pick this provider:

bodymeanβ€³bodymeanβ€³
Sun0.180Jupiter0.239
Moon0.169Saturn0.251
Mercury0.180Uranus0.267
Venus0.180Neptune0.503
Mars0.178

The inner bodies sit in a tight band at ~0.18β€³ and the error then grows monotonically outward. That is the truncation, and it is a property of how the series is cut rather than of any one planet: scripts/generate_vsop87a.py drops terms below a uniform absolute amplitude of 1e-7 AU, applied identically to all eight. Saturn is not cut harder than Mercury β€” it retains the most terms of any planet, 7,530 of 30,046. But an absolute cut leaves a residual whose size scales with the orbit, so converting each body's angular error back into a position error gives 9Γ— the threshold at 1 AU, 60Γ— at Jupiter, 116Γ— at Saturn, 249Γ— at Uranus and 734Γ— at Neptune.

Tightening the threshold would shrink this at the cost of the coefficient set, which is what makes this provider viable in WASM and at the edge. If you need the outer planets to sub-0.1β€³, use SpkReader, where they are the best bodies rather than the worst: Jupiter 0.083β€³ and Saturn 0.061β€³ against the same oracle, versus ~0.13β€³ for the inner planets.

Until 2026-08-20 those figures were 2.06β€³ mean and 24.22β€³ worst, and this README attributed the gap to VSOP87A being a truncated theory. That was wrong, and the wording protected a defect of ours: the analytical provider answered EarthMoonBarycenter with VSOP87A's Earth-centre series, so the observer sat 4,671 km off, and earth_state divided a barycentre-relative Moon by 1 + EMRAT instead of EMRAT for a further 56.8 km. Both are fixed. The second one moved the SPK path too, 0.106β€³ β†’ 0.103β€³.

ELP/MPP02 Moon β€” lunar_horizons.rs: 0.015β€³ at J2000, 0.020–0.053β€³ across 1500–2500 CE.

What is not measured

  • House cusps are not validated against any external reference.
  • No ayanamsha is validated against another implementation, and that is deliberate. All eleven are derived forward from a primary β€” a chapter, a committee, a proposer's own paper, or a star catalogue β€” and each reproduces its own anchor to 1e-9Β° and, where its primary documents one, its own zero year. What is not claimed is agreement with anyone else's numbers: comparing against them would be the reverse-engineering this re-derivation exists to undo. See docs/audit/2026-08-17-ayanamsha-cleanroom/.
  • Dasha and nakshatra tests are invariant tests, not external comparisons: they verify that BPHS constants sum to 120 years and that boundaries tile the circle.
  • Two Shadbala components are partial, and the gap is in the tool description, not just here. Sthana Bala carries four of its five sub-components β€” Uchcha, Ojhayugma, Kendradi and Drekkana β€” but not Saptavargaja Bala, which needs a Moolatrikona degree table and a panchadha maitri derivation we do not yet hold from a primary. Kala Bala covers Nathonnatha and Paksha only. Dig, Cheshta, Naisargika and Drik Bala are whole.

Technical report

Read the full README β†’View source on GitHub β†’

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Frequently Asked Questions about Vedaksha

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "vedaksha": { "command": "npx", "args": ["-y", "vedaksha"] } }

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Technical Specs & Signals

CategoryπŸ’»Developer Tools
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
Last updatedSep 7, 2026
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
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Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

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