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mechreaper007x

ClipACanvas

by mechreaper007x
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Python
Hybrid

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.4

  • Disambiguation4/5

    The tools are clearly differentiated by their output method (base64 vs file), and descriptions state the difference explicitly. However, the core functionality is identical, which could cause confusion if an agent doesn't read carefully.

    Naming Consistency5/5

    Both tools follow a consistent pattern: 'render_video' base name with a suffix '_to_file' for the file variant. The naming is clear and predictable.

    Tool Count3/5

    With only two tools, the server feels thin for a video rendering service. While it covers the basic render operation, additional tools for preview or status would make it more robust.

    Completeness3/5

    The server only offers rendering with two output formats. There are no tools for checking render status, managing rendered files, or handling different video settings, which are notable gaps.

  • Average 4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 58 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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.

  • This repository includes a glama.json configuration file.

  • If you are the author, simply .

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    Annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true) are consistent with description. Description adds details about external processes (Chromium, FFmpeg) and output format, providing behavioral context beyond annotations.

    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?

    Three concise sentences cover purpose, supported features, and output format without redundancy. Every sentence earns its place.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description explains purpose, supported features, output format, and uses external tools. It lacks details on parameter interactions (e.g., min/max_duration) but schema covers defaults. No output schema, but description sufficiently conveys return value. Overall adequate for complexity.

    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 coverage is 100% with descriptions for all 7 parameters. Description adds only minor extra guidance on the 'code' parameter format, so it provides limited added value beyond the schema.

    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?

    Description clearly states the tool renders HTML/CSS/JS to an MP4 video using Chromium and FFmpeg, and specifies supported animations. It distinguishes from sibling 'render_video_to_file' by mentioning the base64-encoded output.

    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?

    Description implies usage context (base64 output) but does not explicitly compare with sibling or state when to use this tool over alternatives. No when-not-to-use guidance is provided.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    Annotations already indicate destructiveHint=true, so the agent knows this tool writes files. The description adds that it saves MP4 to a file path, but does not disclose additional traits like overwrite behavior or disk space requirements. No contradiction with annotations.

    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 two sentences, efficiently front-loading the core difference from the sibling tool. Every word adds value, with no redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a tool with 7 parameters and no output schema, the description is sufficient: it explains the key behavior, use cases, and difference from sibling. It lacks details on error conditions or performance, but the annotations and schema cover most essential aspects.

    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?

    Input schema has 100% description coverage, so parameters are well-documented. The description mentions output_path but adds no new meaning beyond the schema. Baseline of 3 is appropriate per scoring guidelines.

    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 that this tool renders video to a file on disk, contrasting it with render_video which returns base64. The verb 'render' and resource 'video to file' are specific, and the sibling distinction is explicitly made.

    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 context for when to use this tool: 'Useful for integrating with other tools or when dealing with large videos.' It implies when not to use (e.g., when base64 is needed) but does not explicitly state exclusions.

    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.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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