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clipsense

ClipSense MCP Server

by clipsense
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TypeScript
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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. The tool 'analyze-video' has a singular, well-defined purpose that cannot be confused with any other tool in the set.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'analyze-video' follows a clear verb_noun pattern, and there are no other tools to create inconsistency.

    Tool Count2/5

    A single tool is too few for a server named 'ClipSense MCP Server' that implies a broader video analysis or debugging domain. While the tool is well-described, the server lacks additional tools for related operations like listing videos, managing analyses, or handling different file types beyond analysis, making it feel thin and incomplete for its apparent scope.

    Completeness2/5

    The server is severely incomplete for video analysis and debugging. It only provides analysis, with no tools for uploading, listing, deleting, or managing video files, nor for handling analysis results or integrating with development workflows. This creates significant gaps that will likely cause agent failures in real-world use.

  • Average 3.8/5 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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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?

    No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it reads local files (implies no network calls), lists supported video formats, mentions AI-powered analysis, and specifies the types of issues identified. However, it doesn't cover important aspects like performance characteristics (processing time), error handling (what happens with invalid files), or output format details. The description adds value but leaves gaps in behavioral context.

    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 with the core purpose in the first sentence. Each subsequent sentence adds value: supported formats, analysis capabilities, supported platforms, and usage guidance. There's minimal redundancy, though the final sentence could be slightly more concise. Overall, it's well-structured with information density.

    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 (video analysis with AI) and lack of both annotations and output schema, the description does an adequate but incomplete job. It covers the what and when reasonably well but lacks details about the analysis output format, limitations (beyond file size/duration in schema), error conditions, and processing behavior. For a tool with no structured output documentation, more completeness would be beneficial.

    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%, providing good documentation for both parameters. The description doesn't add significant meaning beyond the schema - it mentions video files and analysis questions generally but doesn't elaborate on parameter usage, constraints, or examples beyond what's in the schema descriptions. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate with additional parameter insights.

    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 tool's purpose with specific verbs ('analyze video files', 'reads local video files', 'provides AI-powered analysis') and resources ('video files on the user's computer', 'mobile app bugs'). It distinguishes what it analyzes (video files showing app bugs) and what it provides (error identification, crash detection, UI issue spotting, code fix suggestions). No siblings exist, but the description is sufficiently specific.

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

    Usage Guidelines4/5

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

    The description provides clear context for when to use the tool ('when the user asks you to analyze, examine, or debug a video file showing app behavior') and specifies supported platforms (React Native, iOS, Android). However, it doesn't mention when NOT to use it or alternatives (though no siblings exist, so this is less critical). The guidance is explicit but lacks exclusion criteria.

    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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