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ai-zerolab

pymupdf4llm-mcp

by ai-zerolab
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Python

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity. The tool's purpose is clearly described, and an agent will have no difficulty selecting it.

    Naming Consistency5/5

    The single tool name follows a consistent verb_noun pattern in snake_case. There are no other tools to introduce inconsistency.

    Tool Count3/5

    The server has only one tool, which feels thin for a PDF conversion service. While it covers the core conversion task, a slightly richer set (e.g., 3-5 tools) would be more appropriate.

    Completeness2/5

    The tool converts PDF to markdown with optional image extraction, but lacks other common operations like text extraction without conversion, page range selection, or batch processing. Significant gaps exist.

  • Average 4.1/5 across 1 of 1 tools scored.

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

  • This repository is archived. Archived repositories automatically receive an F maintenance tier.

  • This repository is licensed under BSD 3-Clause.

  • This repository includes a README.md file.

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

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How to sync the server with GitHub?

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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?

    With no annotations, the description must carry the transparency burden. It discloses that the tool also converts PDF to images and saves them, and that absolute paths are required. However, it does not mention side effects (e.g., overwriting existing files), error handling, or permissions. It provides adequate but not comprehensive behavioral insight.

    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 fairly concise with 3-4 sentences. It front-loads the main purpose and then provides details. The full URL is slightly lengthy but appropriate for reference. It earns its sentences.

    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 (context signal), the description covers the essential conversion behavior, image generation, and practical usage tips for large files. It does not address edge cases like encrypted PDFs or invalid paths, but is sufficient for typical use.

    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 100%, so each parameter has a description. The description reinforces the absolute path requirement and adds context: image_path is optional with default behavior, save_path controls whether output is a string or file path, and advice for large files. This adds value 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 converts a PDF file to markdown format using a specific library (pymupdf4llm). It specifies the verb 'converts' and the resource 'PDF file to markdown', and also mentions additional behavior like converting to images. With no sibling tools, it is unambiguous.

    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 explicit guidance: use absolute paths for all parameters, and for larger PDF files use save_path to save the markdown file and read it partially. It does not discuss alternatives (none exist) but gives practical usage context.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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