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OpenTorah-ai

Sefaria Jewish Library MCP Server

by OpenTorah-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Sefaria 犹太图书馆 MCP 服务器

铁匠徽章一个 MCP（模型上下文协议）服务器，提供对 Sefaria 库中犹太文本的访问。该服务器使大型语言模型能够通过标准化接口检索和引用犹太文本。

特征

  • 通过参考检索犹太文本

  • 检索给定文本的评论

Related MCP server: Sefaria MCP Server

安装

需要 Python 3.10 或更高版本。

通过 Smithery 安装

要通过Smithery自动为 Claude Desktop 安装 Sefaria Jewish Library：

npx -y @smithery/cli install mcp-sefaria-server --client claude

克隆存储库

git clone https://github.com/sivan22/mcp-sefaria-server.git
cd mcp-sefaria-server

运行服务器

可以直接运行服务器：

uv --directory path/to/directory run sefaria_jewish_library

或者通过支持模型上下文协议的 MCP 客户端。对于 claude 桌面应用程序和 cline，您应该使用以下配置：

{
  "mcpServers": {        
      "sefaria_jewish_library": {
          "command": "uv",
          "args": [
              "--directory",
              "C:/dev/mcp-sefaria-server",
              "run",
              "sefaria_jewish_library"
          ],
          "env": {
            "PYTHONIOENCODING": "utf-8" 
          }
      }
  }
}

可用工具

服务器通过MCP接口提供以下工具：

获取文本

通过参考检索特定的犹太文本。

例子：

reference: "Genesis 1:1"
reference: "שמות פרק ב פסוק ג"
reference: "משנה ברכות פרק א משנה א"

获取评论

检索给定文本的评论列表。

例子：

reference: "Genesis 1:1"
reference: "שמות פרק ב פסוק ג"
reference: "משנה ברכות פרק א משנה א"

发展

该项目使用：

要求

  • Python >= 3.10

  • MCP SDK >= 1.1.1

  • Sefaria API

执照

MIT 许可证

Available Tools

2 tools
get_commentariesC

get a list of references of commentaries for a jewish text

ParametersJSON Schema
NameRequiredDescriptionDefault
referenceYesthe reference of the jewish text, e.g. 'שולחן ערוך אורח חיים סימן א' or 'Genesis 1:1'

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('get a list of references') without detailing behavioral traits such as whether this is a read-only operation, potential rate limits, authentication needs, or what format the list returns (e.g., structured data, pagination). For a tool with no annotations, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise and front-loaded, consisting of a single sentence that directly states the tool's purpose. There is no wasted language or unnecessary elaboration, making it efficient and easy to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for effective use. It doesn't explain what the output looks like (e.g., list format, data structure), behavioral constraints, or how it differs from sibling tools. For a tool with no structured metadata, the description should provide more context to compensate, but it falls short.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not add any parameter-specific information beyond what the input schema provides. The schema has 100% description coverage, with the 'reference' parameter clearly documented with examples. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate with additional semantic context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'get a list of references of commentaries for a jewish text'. It specifies the verb ('get'), resource ('commentaries'), and target ('jewish text'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this from sibling tools like 'get_text' or 'search_texts', which might also retrieve text-related information.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_text' or 'search_texts', nor does it specify prerequisites, exclusions, or contextual cues for selecting this tool over others. The user must infer usage from the purpose alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_textC

get a jewish text from the jewish library

ParametersJSON Schema
NameRequiredDescriptionDefault
referenceYesThe reference of the jewish text, e.g. 'שולחן ערוך אורח חיים סימן א' or 'Genesis 1:1'

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the action ('get') but doesn't describe traits like whether this is a read-only operation, if it requires authentication, what happens on errors (e.g., invalid reference), or rate limits. The description is minimal and lacks essential behavioral context for a tool with no annotation support.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded in a single sentence ('get a jewish text from the jewish library'), with zero wasted words. It efficiently states the core purpose without unnecessary elaboration, making it easy to parse. However, it's slightly under-specified given the lack of sibling differentiation and behavioral details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a read operation with one parameter) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects, error handling, or output format, leaving gaps for an agent to invoke it correctly. With no structured support, the description should provide more context but falls short.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'reference' parameter well-documented in the schema (including examples like 'Genesis 1:1'). The description adds no additional parameter semantics beyond what the schema provides—it doesn't explain reference formats, constraints, or usage. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose ('get a jewish text from the jewish library'), which is clear but vague. It specifies the verb ('get') and resource ('jewish text'), but doesn't differentiate from siblings like 'search_texts' or 'get_commentaries'—it's unclear if this fetches a single text by reference versus searching. The purpose is understandable but lacks specificity for sibling distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, exclusions, or compare to sibling tools like 'search_texts' (which might handle broader queries) or 'get_commentaries' (which might retrieve related content). Usage is implied from the name and description alone, with no explicit context for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedget_commentaries
    • First observedget_text
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_commentaries retrieves commentary references for a text, while get_text retrieves the text itself. There is no overlap or ambiguity between these operations, making it easy for an agent to select the correct tool based on the need.

Naming Consistency5/5

Both tools follow a consistent verb_noun naming pattern (get_commentaries and get_text), using the same verb 'get' and descriptive nouns. This uniformity makes the tool set predictable and easy to understand at a glance.

Tool Count2/5

With only 2 tools, the server feels under-scoped for a library domain that typically involves more operations like search, update, or categorization. While the tools cover basic retrieval, the count is too low to support comprehensive interactions with a Jewish library, limiting agent capabilities.

Completeness2/5

The tool set is severely incomplete for a library server, lacking essential operations such as search, filtering, creation, or management of texts and commentaries. Agents will face dead ends when trying to perform common library tasks beyond simple retrieval, leading to potential failures.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

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