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collactivelabs

Gemini Image & Video Generation MCP

by collactivelabs
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JavaScript
Remote

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct media type and input condition: image, video, and video-from-image. No overlap or ambiguity between the three.

    Naming Consistency5/5

    All tools follow a clear generate_<target> pattern, with the third adding a descriptive suffix 'from_image' that still fits the convention. Consistent and predictable.

    Tool Count4/5

    Three tools is on the lower end but appropriate for a focused media generation server covering the core generation capabilities. Not excessively thin.

    Completeness4/5

    The server covers primary generation workflows (image, video, video from image). Minor gaps like image editing or video variations exist but are not essential for the stated purpose.

  • Average 2.9/5 across 3 of 3 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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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

  • Behavior2/5

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

    With no annotations provided, the description must bear the full burden of behavioral disclosure, but it only states the core action. It does not disclose what happens to the generated video (e.g., whether it is saved to the filesystem), the output format, or any required authentication or rate limits. The description also does not mention the 'save' parameter's default behavior.

    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 a single, front-loaded sentence that gets to the point quickly. It is structurally efficient and easy to parse, although it may be too terse for a tool with 6 parameters and no output schema.

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

    Completeness1/5

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

    The description is severely incomplete for a tool with 6 parameters and no output schema. It lacks critical information: how the initial image is supplied, what the return value or output looks like, when to adjust sampling parameters, and any side effects. The absence of an image parameter in the schema further compounds this incompleteness.

    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?

    Although the schema covers 100% of parameters with descriptions, the tool description introduces an 'initial image' concept that is not present in the schema, creating confusion. It also does not explain the sampling parameters (topK, topP, temperature) or how they affect results, leaving the agent to rely solely on the schema without additional context.

    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?

    The description clearly states the action (generate a video) and the resource (from an initial image using Gemini Veo 2.0), which distinguishes it from siblings like generate_image and generate_video. However, the phrase 'from an initial image' is not reflected in the input schema, which has no image parameter, making the purpose slightly ambiguous.

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

    Usage Guidelines2/5

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

    The description gives no explicit guidance on when to use this tool vs. alternatives, and does not explain how to provide the 'initial image' (since no image parameter exists in the schema). It fails to mention prerequisites, exclusions, or any context for choosing this tool over generate_video.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the basic action without mentioning side effects, output format, execution time, or save behavior, which is a significant gap.

    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, focused sentence with no redundancy. It front-loads the purpose clearly and contains no unnecessary text.

    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?

    Despite full schema coverage, the tool has no annotations and no output schema. The description does not explain return values, usage context, or differentiation from siblings. For a generative tool that may save files, more contextual information is needed.

    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%, so all six parameters are well documented in the schema. The description itself adds no parameter details, but the baseline of 3 is appropriate since the schema handles the heavy lifting.

    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?

    The description clearly states the tool generates a video using Google Gemini Veo 2.0, providing a specific verb and resource. It does not explicitly differentiate from sibling tools like generate_video_from_image, but the mention of the model implies a direct text-to-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 Guidelines2/5

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

    No guidance is provided on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or how it differs from generate_video_from_image.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action and model, omitting any context about side effects such as saving to filesystem, return format, or permission requirements.

    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 concise sentence with no unnecessary words. It efficiently states the core purpose.

    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?

    Given there is no output schema and no annotations, the description does not explain the return value, output format, or any operational details. It relies entirely on the schema, which is insufficient for a generation tool.

    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%, so the parameters are already fully documented in the schema. The description adds no additional meaning about parameter usage or format.

    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 the specific verb 'generate' with the resource 'an image' and names the underlying model 'Google Gemini'. It clearly distinguishes from sibling tools generate_video and generate_video_from_image, which target video.

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

    Usage Guidelines2/5

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

    The description provides no explicit guidance on when to use this tool versus the video generation siblings. The name and verb imply image generation, but there is no mention of alternatives or 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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