Sefaria MCP Server

A modern MCP (Model Context Protocol) server for accessing the Jewish library via the Sefaria API.

What does this server do?

This server exposes the Sefaria Jewish library as a set of 14 MCP tools, allowing LLMs and other MCP clients to:

Primary Tools:

• get_text - Retrieve Jewish texts by reference (e.g., "Genesis 1:1")

• text_search - Search across the entire Jewish library

• get_current_calendar - Get situational Jewish calendar information. Accepts diaspora (true for Diaspora, false for Israel) for weeks when Torah reading schedules diverge.

Core Tools:

• get_links_between_texts - Find cross-references and connections between texts

• search_in_book - Search within a specific book or text work

• search_in_dictionaries - Search Jewish reference dictionaries

Support Tools:

• get_english_translations - Retrieve all available English translations for a text

• get_topic_details - Retrieve detailed information about topics in Jewish thought

• clarify_name_argument - Autocomplete and validate text names, book titles, and topics

• clarify_search_path_filter - Convert book names to proper search filter paths

Structure Tools:

• get_text_or_category_shape - Explore the hierarchical structure of texts and categories

• get_text_catalogue_info - Get bibliographic and structural information (index) for a work

Manuscript Tools:

• get_available_manuscripts - Access historical manuscript metadata and image URLs

• get_manuscript_image - Download and process specific manuscript images

All endpoints are optimized for LLM consumption (compact, relevant, and structured responses).

Related MCP server: Sefaria Jewish Library MCP Server

What is MCP?

MCP (Model Context Protocol) is an open protocol for connecting Large Language Models (LLMs) to external tools, APIs, and knowledge sources. It enables LLMs to retrieve, reference, and interact with structured data and external services in a standardized way. Learn more in the MCP documentation.

How to Run

Prerequisites

• Python 3.10+

• Docker (optional, for containerized deployment)

Local Development

1. Install dependencies:

   pip install -e .

2. Run the server:

   python -m sefaria_mcp.main

   The server will be available at http://127.0.0.1:8088/sse by default. Set SEFARIA_MCP_PORT to override the SSE/API port (e.g., SEFARIA_MCP_PORT=8089 python -m sefaria_mcp.main). Prometheus metrics bind separately on SEFARIA_MCP_METRICS_PORT (default 9090).

Docker

1. Build the image:

   docker build -t sefaria-mcp .

2. Run the container:

   docker run -d --name sefaria-mcp \
       -e SEFARIA_MCP_PORT=8089 \
       -e SEFARIA_MCP_METRICS_PORT=9090 \
       -p 8089:8089 \
       -p 9090:9090 \
       sefaria-mcp

   The server will be available at http://localhost:8089/sse and metrics at http://localhost:9090/ (adjust the port mappings as needed).

Usage

• Connect your MCP-compatible client to the /sse endpoint.

• All tool endpoints are available via the MCP protocol.

Monitoring

• Prometheus metrics are exposed via the standalone HTTP server started on SEFARIA_MCP_METRICS_PORT (defaults to 9090).

• Scrape http://localhost:9090/ (or your configured host/port). Metrics include:

  • mcp_tool_calls_total{tool_name,status} – call counts per tool and status.

  • mcp_tool_duration_seconds{tool_name} – histogram of per-call durations.

  • mcp_tool_payload_bytes{tool_name} – histogram of response payload sizes.

  • mcp_errors_total{tool_name,error_type} – per-tool error counts.

  • mcp_active_connections – current SSE connection gauge.

  • Standard FastAPI instrumentation (request rate, latency, status codes, in-progress requests, etc.) from prometheus_fastapi_instrumentator.

Commit Hygiene

This repo uses semantic commits with the fix, feat, and chore keywords.

Acknowledgments

Special thanks to @Sivan22 for pioneering the first Sefaria MCP server (mcp-sefaria-server), which inspired this project and the broader effort to make Jewish texts accessible to LLMs and modern AI tools.

License

MIT