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zachswift615

Audio Analysis MCP Server

by zachswift615
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Python
Local

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion between tools. The single tool's purpose is clearly defined as audio analysis.

    Naming Consistency5/5

    With only one tool, naming consistency is trivially maintained. The name 'audio_analyze' appropriately describes its function.

    Tool Count2/5

    The server has only one tool for a broad domain (audio analysis), which is too few for the apparent scope. A more modular set of tools (e.g., separate tools for each operation) would be appropriate.

    Completeness3/5

    The single tool covers many common audio analysis operations via the 'ops' parameter, but bundling them into one tool makes the surface less intuitive and may miss operations like audio input handling or transcription.

  • Average 3.6/5 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

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  • 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

  • Behavior3/5

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

    The description indicates the tool performs analysis, which implies read-only behavior, but does not explicitly state side effects, error conditions, or performance characteristics. With no annotations, more detail would be beneficial.

    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—one short sentence and a clear list of operations. No wasted words, and the structure is easy to parse.

    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?

    The description does not explain what each operation returns, how errors are handled, or the role of the optional 'path2' parameter. For a tool with multiple complex operations and no output schema, this is insufficient.

    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 input schema already provides descriptions for all three parameters (100% coverage). The description only repeats the enum values for 'op' and adds no new meaning beyond what the schema offers. Baseline score of 3 is appropriate.

    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 'Analyze audio' and lists all supported operations, making the tool's purpose specific and unambiguous. The verb+resource pattern is effective.

    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?

    While the list of operations is provided, there is no guidance on when to use each operation or how to choose between them (e.g., compare vs diff). The description implies the tool is for general audio analysis but lacks explicit usage context.

    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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