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302ai

302AI File Parser MCP Server

by 302ai

Server Quality Checklist

58%
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. The tool's purpose is clearly distinct as it is the sole operation available.

    Naming Consistency5/5

    The single tool name follows a clear verb_noun pattern (parseFileToText), and with only one tool, consistency is inherently perfect as there are no other names to compare against.

    Tool Count2/5

    A single tool is generally too few for a server named 'File Parser', as this suggests a domain that might include operations like parsing different file types, handling errors, or extracting metadata. The scope feels incomplete with just one basic parsing function.

    Completeness2/5

    The server's name implies a broader file parsing capability, but the tool set only includes parsing files to text. Obvious gaps include support for different file formats (e.g., PDF, DOCX), structured data extraction, or error handling, making the surface severely incomplete for the apparent purpose.

  • Average 2.9/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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?

    No annotations are provided, so the description carries full burden. It mentions parsing and returning text, but doesn't disclose critical behavioral traits: supported file formats (covered in schema), parsing limitations (e.g., large files, encoding issues), error handling, or performance characteristics. The description is too vague about how parsing works and what happens in edge cases.

    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 concise and front-loaded in a single sentence: 'Provide a file url, parse the file to text, return the text as a string.' It efficiently states the core functionality without unnecessary words. However, it could be slightly more structured by separating input, action, and output more clearly.

    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?

    Given no annotations and no output schema, the description is incomplete for a tool that performs file parsing. It doesn't explain the return value format beyond 'as a string' (e.g., structured text, encoding), doesn't mention potential errors or limitations, and lacks details on behavioral aspects. For a tool with 1 parameter but complex underlying functionality, this leaves significant gaps.

    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?

    Schema description coverage is 100%, so the schema fully documents the single parameter 'url' with its type and supported formats. The description adds no additional meaning beyond what's in the schema—it merely repeats 'Provide a file url' without extra context. This meets the baseline of 3 when schema coverage is high.

    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: 'parse the file to text, return the text as a string.' It specifies the action (parse) and resource (file), and mentions the input (file url) and output (text string). However, it doesn't distinguish from siblings since none exist, so it can't achieve the full 5-point differentiation.

    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 minimal usage guidance: 'Provide a file url' indicates when to use it, but offers no context about when not to use it or alternatives. With no sibling tools, it can't specify alternatives, but it lacks any prerequisites, limitations, or error conditions that would help an agent decide appropriateness.

    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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  • Evaluate tool definition quality.

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