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svretina

nougat-mcp

by svretina
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have entirely distinct purposes: one retrieves configuration settings, the other parses PDFs.

    Naming Consistency5/5

    Both tools use the consistent verb_noun pattern with underscores, e.g., get_output_settings and parse_research_paper.

    Tool Count4/5

    With only two tools, the server is minimal but appropriate for its focused scope on PDF parsing with a settings helper.

    Completeness4/5

    The server provides the core functionality (parse and settings) for its domain, though it lacks auxiliary features like model listing.

  • Average 3.8/5 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 GPL 3.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • Behavior3/5

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

    The description reveals that it reads from environment variable or a file, which is helpful. However, it does not mention failure modes (e.g., missing file), whether it is read-only, or any other side effects. With no annotations, a bit more detail would improve transparency.

    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 exceptionally concise with two short sentences, no filler, and front-loaded with the core purpose. Every word earns its place.

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

    Completeness3/5

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

    Given the simplicity (no params, no output schema), the description covers the basic functionality. However, it does not specify the format or structure of the returned settings, which might be needed for agents to use the output effectively.

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

    Parameters4/5

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

    There are no parameters, so schema coverage is 100% by default. The description adds meaning by specifying data sources (NOUGAT_MCP_SETTINGS or ./settings.json), which is useful beyond the empty schema.

    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 ('Return') and the resource ('resolved output settings'), with a clear purpose for agents to adapt behavior. While the sibling tool is unrelated, the purpose is specific enough to differentiate.

    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?

    There is no explicit guidance on when to use this tool or when to avoid it. The description lacks context about prerequisites or alternatives, such as if settings might be unavailable or how often to call it.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It discloses use of Meta's Nougat model and conversion of visual structures, but lacks details on error handling, performance, or file size limits. The Returns section provides some clarity.

    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 well-structured: a clear purpose sentence followed by argument and return descriptions. It is appropriately sized but could be slightly more concise by removing redundant phrasing.

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

    Completeness4/5

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

    Given the presence of an output schema (not shown but noted), the description adequately covers input parameters and return format. It does not discuss errors or edge cases, but is generally complete for a straightforward parsing tool.

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

    Parameters4/5

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

    Schema coverage is 0%, so the description must explain parameters. It clearly describes 'file_path' as absolute path and elaborates on 'output_format' enum values: 'default', 'mmd', and 'md' with their behaviors. This adds meaningful context beyond the schema.

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

    Purpose5/5

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

    The description clearly states the tool's purpose: high-accuracy OCR for academic papers and scientific PDFs, converting visual structures to Markdown. It distinguishes itself from the sibling tool 'get_output_settings' by being a parsing tool rather than a settings retrieval tool.

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

    Usage Guidelines4/5

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

    The description provides guidance on when to use each output format option and mentions the 'default' utilizes settings.json. However, it does not specify prerequisites (e.g., file existence) or when to avoid using this tool.

    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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