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FastMCP Server for Sefaria

sefaria-mcp 최신버젼 다운로드

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# Sefaria MCP Server

A modern [MCP (Model Context Protocol)](https://github.com/ai21labs/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).

## 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](https://modelcontextprotocol.io/).

## How to Run

### Prerequisites
- Python 3.10+
- Docker (optional, for containerized deployment)

### Local Development

1. **Install dependencies:**
    ```bash
    pip install -e .
    ```
2. **Run the server:**
    ```bash
    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:**
    ```bash
    docker build -t sefaria-mcp .
    ```
2. **Run the container:**
    ```bash
    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](https://github.com/Sivan22) for pioneering the first Sefaria MCP server ([mcp-sefaria-server](https://github.com/Sivan22/mcp-sefaria-server)), which inspired this project and the broader effort to make Jewish texts accessible to LLMs and modern AI tools.

## License
MIT

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