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OpenTorah-ai

Sefaria Jewish Library MCP Server

by OpenTorah-ai
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • 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.

  • Average 2.9/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • 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 mentions retrieving a list but doesn't specify if this is a read-only operation, how results are formatted, if there are rate limits, or any other behavioral traits. This leaves significant gaps for a tool with no annotation coverage.

    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 a single, efficient sentence that directly states the tool's function without any unnecessary words. It is appropriately sized and front-loaded, making it easy to understand quickly.

    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. It doesn't explain what the returned list contains (e.g., format, structure), any limitations, or how it differs from the sibling tool. For a tool with no structured support, more context is needed to be fully helpful.

    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?

    Schema description coverage is 100%, so the schema already documents the single parameter 'reference' with examples. The description adds no additional meaning beyond what the schema provides, such as clarifying the scope or format of commentaries, so it meets the baseline for high schema coverage.

    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 action ('get a list of references') and resource ('commentaries for a jewish text'), making the purpose understandable. However, it doesn't explicitly differentiate from the sibling tool 'get_text', which might also retrieve text-related information, so it doesn't reach the highest score.

    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 like 'get_text', nor does it mention any prerequisites or exclusions. It only states what the tool does, leaving usage context implied at best.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries full burden. It states the tool 'gets' a text, implying a read operation, but doesn't disclose behavioral traits like authentication needs, rate limits, error handling, or what 'get' entails (e.g., returns full text, metadata, or formatting). The description is minimal and lacks context beyond the basic action.

    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 a single, efficient sentence: 'get a jewish text from the jewish library'. It's appropriately sized and front-loaded with the core action. There's no wasted verbiage, though it could be more structured with additional context.

    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 no annotations, no output schema, and a simple input schema, the description is incomplete. It lacks details on what the tool returns (e.g., text content, format), error conditions, or behavioral context. For a tool with minimal structured data, the description should provide more completeness to guide effective use.

    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?

    Schema description coverage is 100%, with the parameter 'reference' fully documented in the schema. The description doesn't add any parameter semantics beyond what the schema provides (e.g., no examples or usage tips). Baseline is 3 since the schema does the heavy lifting, but the description doesn't compensate or enhance parameter understanding.

    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 jewish text from the jewish library' specifies the verb 'get' and resource 'jewish text' with source context. It doesn't explicitly differentiate from the sibling 'get_commentaries', but the resource type distinction is implied. The description avoids tautology by not just restating the tool name.

    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 the sibling tool 'get_commentaries' or any other context for selection. Usage is implied only by the tool's name and purpose, with no explicit when/when-not instructions or prerequisites stated.

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

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