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FutureUnreal

mcp-pdf2md

by FutureUnreal
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

Server Quality Checklist

67%
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: one handles local file paths, the other handles URLs. The naming and descriptions make it impossible to confuse which tool to use for a given input source.

    Naming Consistency5/5

    Both tools follow an identical verb_noun pattern (convert_pdf_file and convert_pdf_url) with consistent snake_case formatting. The naming is perfectly predictable across the toolset.

    Tool Count3/5

    With only two tools, the server feels minimal but functional. While it covers the core conversion task for both local files and URLs, the count is borderline thin for a PDF-to-Markdown domain that could potentially include more operations like batch processing, format options, or metadata extraction.

    Completeness4/5

    The server covers the essential conversion operation for both local files and remote URLs, which are the two main input sources for PDFs. The minor gap is the lack of additional PDF manipulation or output customization tools, but agents can perform basic conversions without dead ends.

  • Average 3.1/5 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the OCR capability and return format (dict with conversion result information), but lacks critical details: whether this is a read-only operation, what happens with invalid files, if there are size/time limitations, what specific information the result dict contains, or error handling behavior.

    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 reasonably concise with clear sections (Args, Returns) and front-loaded purpose statement. However, the 'Args' and 'Returns' labels add some redundancy since this information is partially available in the schema, and some sentences could be more efficiently worded.

    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?

    For a file conversion tool with 2 parameters, no annotations, and no output schema, the description is insufficient. It lacks information about file format requirements, conversion quality, error conditions, output structure details, performance characteristics, or how the tool differs from its sibling. The return value description ('dict: Conversion result information') is particularly vague given no output schema exists.

    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?

    The description provides basic parameter information in the Args section, explaining that file_path accepts local paths or lists with various separators, and enable_ocr defaults to True. However, with 0% schema description coverage, it doesn't fully compensate by explaining path format requirements, file accessibility constraints, or what OCR actually does in this context beyond the boolean toggle.

    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: converting PDF files to Markdown format, with support for single files or lists. It specifies the resource (PDF files) and action (convert to Markdown), though it doesn't explicitly differentiate from the sibling tool 'convert_pdf_url' which likely handles URL-based PDFs rather than local files.

    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. While it mentions support for single files or lists, it doesn't explain when to choose this over 'convert_pdf_url' or other potential conversion tools. There's no mention of prerequisites, limitations, or typical use cases.

    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 the full burden of behavioral disclosure. It mentions OCR support with a default setting, which adds some context, but fails to describe critical behaviors such as rate limits, authentication requirements, error handling, or what the conversion result information includes. For a tool that processes external URLs and performs conversion, this is a significant gap in transparency.

    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 appropriately sized and front-loaded, starting with the core purpose followed by parameter details in a structured format. Every sentence adds value, with no redundant information. However, the use of 'dict' in the returns section is slightly vague, though this is mitigated by the lack of an output schema.

    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 tool's complexity (processing PDF URLs with OCR options) and the absence of annotations and output schema, the description is minimally adequate. It covers the basic purpose and parameters but lacks details on behavioral traits, error cases, and output structure. This leaves gaps that could hinder an agent's ability to use the tool effectively in varied contexts.

    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?

    The description adds meaningful semantics beyond the input schema, which has 0% description coverage. It explains that 'url' can be a single URL or a list separated by spaces, commas, or newlines, and clarifies the default value and purpose of 'enable_ocr'. This compensates well for the schema's lack of descriptions, making the parameters understandable without relying on the schema alone.

    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: converting PDF URLs to Markdown format. It specifies the resource (PDF URLs) and the action (convert to Markdown), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling tool 'convert_pdf_file' beyond mentioning URL vs. file handling.

    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 by mentioning support for single URLs or URL lists, but it doesn't provide explicit guidance on when to use this tool versus alternatives like 'convert_pdf_file'. No when-not-to-use scenarios or prerequisites are mentioned, leaving the agent to infer context from the tool name and description alone.

    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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