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gavinHuang

PDF2MD MCP Server

by gavinHuang
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

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 confusion or overlap between tools. The tool's purpose is singular and clearly defined as converting PDFs to Markdown, leaving no room for misselection.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (convert_pdf_to_markdown), which is consistent with itself. Since there are no other tools, there is no inconsistency to evaluate, making it perfectly consistent by default.

    Tool Count2/5

    A single tool is too few for most server purposes, as it limits functionality and may indicate an incomplete or overly narrow scope. For a PDF conversion server, one tool is minimal and could benefit from additional related operations, such as batch processing or format validation.

    Completeness3/5

    The tool covers the core conversion task well, but there are notable gaps in the surface, such as no tools for handling errors, validating input formats, or managing multiple files. While the main function is present, the lack of supporting operations makes the set incomplete for robust PDF-to-Markdown workflows.

  • 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
    • 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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    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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it specifies the conversion uses 'AI sampling', describes default output directory behavior for local files vs. URLs, and outlines the return structure. However, it misses details like error handling, performance limits, or authentication needs.

    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 front-loaded with the core purpose, followed by structured sections for Args and Returns. Every sentence adds value without waste, making it easy to scan and understand quickly. The formatting enhances readability without verbosity.

    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 moderate complexity (2 params, no annotations, but with output schema), the description is largely complete: it covers purpose, parameters, and return values. The output schema handles return structure, so the description doesn't need to duplicate that. It could improve by mentioning potential errors or limitations.

    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 description coverage is 0%, so the description must compensate. It adds meaningful context for both parameters: 'file_path' clarifies it accepts local paths or URLs, and 'output_dir' explains default behaviors based on input type. This goes beyond the bare schema, though it could detail format constraints (e.g., URL protocols).

    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 specific action (convert), resource (PDF file), and target format (Markdown) using 'AI sampling'. It distinguishes the tool's purpose with technical detail about the conversion method, making it immediately understandable without redundancy.

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

    Usage Guidelines3/5

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

    The description implies usage for PDF-to-Markdown conversion but provides no explicit guidance on when to use this tool versus alternatives (e.g., other conversion methods or tools). Since there are no sibling tools, the lack of comparative guidance is less critical, but it still doesn't offer context like prerequisites or typical scenarios.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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