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maxrabin

pdf-image-extractor-mcp

by maxrabin
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Python
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Server Quality Checklist

67%
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  • Latest release: v0.1.4

  • Disambiguation5/5

    Only one tool exists, so no ambiguity between tools is possible.

    Naming Consistency5/5

    Single tool so naming pattern is trivially consistent.

    Tool Count2/5

    One tool for PDF image extraction is too few; the server would benefit from additional tools like listing pages, extracting text, or handling metadata.

    Completeness3/5

    The tool covers basic image extraction but lacks options for page selection, image format control, or handling of edge cases like password-protected PDFs.

  • Average 4.2/5 across 1 of 1 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • 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 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 mentions pagination and return type (list of image contents and summary), but does not elaborate on what 'image contents' means (e.g., format, encoding) or disclose potential side-effects. Since there are no annotations, the description could provide more behavioral context, but it is not misleading.

    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: three sentences with zero wasted words. The most important information (action, pagination, batch recommendation, return type) is front-loaded and clearly ordered.

    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 tool's low complexity (simple extraction with 3 parameters and a basic output schema), the description covers the essential aspects: what it does, how to paginate, and what is returned. It could mention file limitations or format details, but it is otherwise complete for an agent to use correctly.

    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?

    All three parameters are fully described in the schema (100% coverage), so the baseline is 3. The description adds value by explaining the recommendation for batch size (max_images) and pagination context (start_index), which clarifies usage 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 action: 'Extract images from a PDF file with pagination.' The verb 'Extract' and resource 'PDF file' are specific, and the mention of pagination distinguishes this from a simple one-shot extraction. With no sibling tools, this is a strong purpose statement.

    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 usage advice: 'Works best when extracting small batches of images (e.g., 10) at a time.' This guides the agent on batch size. It does not mention when not to use or alternatives, but with no siblings, the guidance is sufficient.

    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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