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nikships

Gemini Omni MCP

by nikships
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Python
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Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    The two tools serve completely distinct purposes: batch_generate for parallel multi-video generation and generate_video for single video creation/editing. There is no overlap in functionality, ensuring clear differentiation.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern in snake_case (generate_video, batch_generate). The naming style is uniform, making it predictable for agents.

    Tool Count4/5

    With only two tools, the server is narrowly scoped but appropriate for its focused purpose of video generation. The tools cover both single and batch workflows without unnecessary bloat.

    Completeness4/5

    The tool surface covers the core generation lifecycle (text-to-video, image-to-video, reference-to-video, editing) and batch processing. Minor gaps exist (no listing of previous interactions or deletion), but these are not essential for the domain.

  • Average 4.1/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 7 commits in the last 12 weeks
    • Last stable release on
    • 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.

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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

  • Behavior4/5

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

    With no annotations, the description discloses key behaviors: conservative parallel batching, batch size cap at 4, and response structure (video.path, interaction_id, etc.). It does not discuss authentication or rate limits, but the tool is a generator so destructive effects are minimal.

    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 brief, with the primary action in the first sentence, followed by use cases and key constraints. Every sentence adds value without redundancy.

    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 has 8 parameters and no schema descriptions, the description is incomplete for parameter semantics. It does mention the output format (JSON with specific fields) and batch behavior, but more detail on each parameter's role is needed for full contextual completeness.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description must explain parameters. It only covers batch_size, aspect_ratio, and reference_image_paths implicitly. Other parameters like task, delivery, enhance_prompt, and duration_seconds are not mentioned, leaving the agent underinformed for 8 parameters.

    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 generates multiple Gemini Omni Flash videos in parallel batches. It explicitly mentions use cases like storyboards and aspect-ratio comparisons, distinguishing it from the sibling tool 'generate_video' which likely handles single video generation.

    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 specific usage scenarios (storyboards, comparisons, variations) and a rationale for batch size cap ('because video jobs are long'), guiding when to use. It implicitly excludes single-video use, but could be more explicit about when to prefer the sibling tool.

    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?

    Discloses limitations (720p, 24fps, watermark, preview quality), unsupported features, region restrictions, and post-success behavior (open video.path). Annotations absent, so description carries full burden and does well.

    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?

    Well-structured with sections and bullets, but lengthy. Front-loads purpose. Every sentence adds value, but slight redundancy in parameter listing.

    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?

    Covers capabilities, parameters, prompt tips, limitations, return values. Output schema exists, but description adds detailed return structure. Complete for a complex tool with 9 parameters and a sibling.

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

    Parameters5/5

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

    All 9 parameters are individually described with context, defaults, and usage nuances (e.g., duration_seconds retry, reference tags). Compensates fully for 0% schema coverage.

    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?

    Clearly states 'Generate or edit MP4 videos' and enumerates four specific capabilities (text_to_video, etc.). Distinguishes from sibling 'batch_generate' by focusing on single generation/editing tasks, though not explicitly contrasted.

    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?

    Provides task categories and prompt tips, but lacks explicit guidance on when to use vs. batch_generate or when to avoid. Usage context is implied rather than stated.

    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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