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wowuz

PDF MCP Server

by wowuz
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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 or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (read_pdf), and since there is only one tool, consistency is inherently perfect with no deviations or mixing of conventions.

    Tool Count2/5

    A single tool is too few for a server named 'PDF MCP Server', which suggests a broader scope for PDF operations. While the tool is useful, the server lacks basic operations like merging, splitting, or converting PDFs, making it feel incomplete and under-scoped.

    Completeness2/5

    The server is severely incomplete for PDF processing; it only offers text extraction but misses essential operations such as creating, editing, merging, splitting, or converting PDFs. This creates significant gaps that will limit agent capabilities in handling PDF workflows.

  • Average 3.9/5 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.

  • 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

  • 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 of behavioral disclosure. It adds valuable context about the backend ('Uses a Python backend (marker-pdf)') and its capabilities ('preserve mathematical notations (LaTeX) and layout structure'), which helps the agent understand the tool's behavior beyond basic functionality. However, it does not mention error handling, performance characteristics, or output format details.

    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 appropriately sized and front-loaded, with three concise sentences that each add value: the core functionality, technical details, and usage context. There is no wasted text, and it efficiently communicates essential information.

    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 moderate complexity (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers purpose and backend details but lacks information on output format, error conditions, or performance limits. Without an output schema, the agent is left uncertain about what the tool returns, which is a significant omission for a read operation.

    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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description does not add any additional meaning or context about the parameters beyond what the schema provides, such as explaining the significance of page ranges or path requirements. Baseline 3 is appropriate when the schema does the heavy lifting.

    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 ('Read and extract text content') and resource ('from a PDF file'), with additional detail about the backend and target use case ('Best for scientific papers'). It distinguishes itself by mentioning preservation of mathematical notations and layout structure, which is valuable even without sibling tools.

    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 context ('Best for scientific papers') but does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or exclusions. With no sibling tools, the bar is lower, but it lacks comprehensive usage instructions.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
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  • Evaluate tool definition quality.

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