SheetShift MCP server

Sahadeva

A clean-room, Cloudflare-native research build for a transparent South Indian AI Jyotish workspace.

Run locally

npm install
npm run dev

The Cloudflare Vite plugin serves the React application and Worker API together. POST /api/chart is deterministic. POST /api/interpret uses the configured Workers AI binding.

AI narration

Narration uses only Cloudflare's native AI binding and Cloudflare-hosted @cf/... models. The default is @cf/zai-org/glm-5.3-flash; AI_MODEL selects it in wrangler.jsonc, and non-@cf/ model names are rejected in favor of that safe default. No AI Gateway, third-party API, or provider token participates in this path. GET /api/ai/status exposes only non-secret readiness metadata, and successful narration identifies the Cloudflare model while keeping calculated evidence immutable.

The consultation surface adds deterministic topic judgments, house exploration, natal Panchanga, convention comparison, Prashna, timing fusion, rectification, and depth endpoints. Topic judgments share one evidence-ledger engine across web, MCP, reports, and the Expo mobile client. Opt-in snapshots store versioned ledgers and a hashed follow-up secret—not names, places, or raw birth input—and calibration remains descriptive rather than predictive.

Connect through MCP

After deployment, compatible clients can connect to:

https://your-worker.example/mcp

The Streamable HTTP endpoint exposes a high-signal catalog led by a comprehensive first-reading dossier. The returned profile reference lets later consultations answer focused questions from the verified natal context without repeating the dossier. It also supports chart reports, Prashna, timing, separate North Indian Ashtakoota and South Indian ten-Porutham compatibility, Panchanga, Muhurta and visual reports. Specialist tools remain directly callable and are documented through an MCP resource. Chart calculation does not require a language model. Public calls are rate-limited; optional scoped Bearer keys provide metering and vault integration. See MCP integration examples.

Clients can call assesspredictionreadiness before interpretation to distinguish implemented calculation from reviewed rules and calibrated prediction. Lal Kitab currently provides source-linked structural inspection. All of its material is retained, including sensitive topics and remedies; reviewed claims can later use graduated caution-led disclosure, while unreviewed personalized output remains withheld. See Prediction quality and MCP gap audit.

The source-only analyzelalkitab workflow now converts natal placements to Lal Kitab fixed houses and returns locators for all relevant planet-house sections without publishing their unreviewed claims. The complete integration inventory is in Lal Kitab integration map.

The web chart surfaces the same fixed-house map, source locators, retained-sensitive-material caution, and an independent calculation-quality audit. MCP clients can use the lalkitabconsultation prompt or compose the equivalent tools from the published workflow resource.

For evidence-first use across AI clients, the MCP now publishes sahadeva://prediction-quality and the evidencefirstprediction prompt. Reviewed-rule search, rights-aware passage discovery, separate-tradition comparison, per-claim auditing, versioned outcome capture and conservative validation reporting are available as typed tools. These improve traceability and abstention; they do not guarantee prediction accuracy or convert unreviewed traditions into validated knowledge.

Shortened. The full README is on GitHub.