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zcsabbagh

manim-mcp

by zcsabbagh
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    render_video and show_demo_video are clearly distinct: one creates a video from user-provided scenes, the other displays a pre-rendered demo. There is no overlap in purpose or output.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (render_video, show_demo_video), making the API predictable and easy to navigate.

    Tool Count3/5

    With only two tools, the server feels minimal for a video rendering domain, but it targets a narrow use case (rendering with voiceover and demoing), so it is borderline acceptable.

    Completeness3/5

    The server lacks operations like listing available scenes, rendering individual scenes without concatenation, or retrieving prior renders, leaving notable gaps for agents needing more granular control over the rendering process.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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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    {
  "$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

  • Behavior3/5

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

    No annotations are present, so the description carries the burden. It discloses that the video is pre-rendered and that no generation is required, implying a non-destructive display action. However, it does not explicitly state read-only behavior, side effects, or failure modes.

    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 a single, front-loaded sentence with no filler. Every phrase contributes meaningful information about the tool's purpose and behavior.

    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 no-parameter demo tool with an output schema, the description provides enough context: what it shows, where it shows it, and that it does not generate. It could add an explicit side-effect statement, but overall this is adequate.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The tool has zero parameters and schema coverage is 100%, so there is no parameter documentation needed. The description adds no parameter-specific meaning, but none is required.

    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 uses a specific verb ('Show') and resource ('pre-rendered demo video inline'), and it clearly distinguishes the tool from its sibling by adding 'No generation needed.' This makes the tool's purpose immediately clear.

    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 states the tool is for testing the MCP video player and contrasts itself with generation via 'No generation needed.' It does not explicitly name the alternative tool (render_video) or provide exclusion criteria, but the usage context is clear.

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

  • Behavior4/5

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

    With no annotations, the description takes full responsibility for disclosing behavior. It reveals that scenes render concurrently, voiceover is auto-synced, and the server auto-fixes common issues like wrong TTS, color, and MathTex usage. This goes beyond basic functionality and helps set expectations, though it doesn't cover all potential failure modes or side effects.

    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 four sentences, each serving a purpose: main action, scene requirements, concurrency behavior, and auto-fix details. There is no redundancy or fluff. It is front-loaded with the primary action and remains skimmable.

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

    Completeness5/5

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

    Given the tool's moderate complexity (2 params, one enum, nested scene objects) and the presence of an output schema, the description covers all necessary behavioral context: parallel rendering, auto-fixing, and voiceover handling. It does not need to explain return values because an output schema exists, and the input schema already documents parameter constraints.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining that each scene's code must have voiceover baked in via manim-voiceover, and that scenes render concurrently. This helps the agent understand the intent behind the code parameter and the parallel nature of the scenes array.

    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 opens with a specific verb+resource+outcome: 'Render one or more Manim scenes in parallel, concatenate them, and return one combined video inline.' This clearly distinguishes it from the sibling tool show_demo_video, which presumably displays a pre-made demo rather than rendering new content.

    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 implies usage: you use this tool to render and combine Manim scenes into a single video. It doesn't explicitly mention alternatives or exclusions, but the clear purpose and the existence of a sibling tool provide enough context for an agent to decide. Since it lacks explicit 'when not to use' guidance, it misses the top score.

    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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