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antoniolg

Gemini Image MCP Server

by antoniolg
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: generate_image creates new images from text descriptions, while edit_image modifies existing images based on text instructions. The descriptions explicitly differentiate them and even cross-reference when to use each, eliminating any potential confusion.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (generate_image, edit_image) with clear action verbs that accurately describe their functions. The naming is perfectly uniform and predictable across the tool set.

    Tool Count3/5

    With only 2 tools, this server feels somewhat thin for an image generation/editing domain. While the tools cover the core operations, additional functionality like image analysis, format conversion, or batch processing might be expected but are absent, making the scope borderline minimal.

    Completeness4/5

    For a basic image AI server, the tools cover the essential create and modify operations well. However, there are minor gaps such as no direct image analysis or metadata tools, and the reliance on external paths for images might limit some workflows, though agents can generally work around these limitations.

  • Average 3.4/5 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.

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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 carries the full burden of behavioral disclosure. It states the tool modifies images using AI, implying mutation, but lacks details on permissions, side effects, rate limits, or output behavior. The mention of saving to an output path is covered in the schema, not behavioral context. This is inadequate for a mutation tool with zero annotation coverage.

    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 concise and front-loaded, with two sentences that directly state the tool's purpose and key parameters. Every sentence earns its place by providing essential information without redundancy or fluff.

    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 the tool's complexity (AI-based image editing with mutation), lack of annotations, and no output schema, the description is incomplete. It fails to address critical behavioral aspects like error handling, output format, or limitations, leaving significant gaps for an AI agent to understand the tool fully.

    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 schema fully documents all three parameters. The description adds minimal value beyond the schema, mentioning the image path and description of changes but not elaborating on semantics. Baseline 3 is appropriate as the schema does the heavy lifting, though the description doesn't compensate for any gaps.

    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's purpose: 'Modify an existing image using Google Gemini AI based on a text instruction.' It specifies the verb ('Modify'), resource ('existing image'), and technology ('Google Gemini AI'), distinguishing it from the sibling tool 'generate_image' which likely creates new images. However, it doesn't explicitly contrast with the sibling beyond implied differences.

    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 guidance on when to use this tool versus alternatives. It mentions the sibling tool 'generate_image' exists but gives no explicit comparison, prerequisites, or exclusions. Usage is implied through the description of modifying existing images, but no clear when/when-not rules are stated.

    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. While it mentions the tool creates images and can use reference images, it lacks critical behavioral details like whether this is a read/write operation, potential rate limits, authentication requirements, error handling, or what the output looks like (e.g., file path, image data). For a generative AI tool with no annotation coverage, this 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 perfectly concise with only two sentences that each earn their place. The first sentence states the core purpose and key optional feature, while the second provides crucial sibling tool differentiation. There's zero wasted text and it's front-loaded with the most important information.

    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 (generative AI with 7 parameters) and lack of both annotations and output schema, the description is incomplete. While it covers purpose and sibling differentiation well, it doesn't address behavioral aspects, output format, or error conditions that would be important for an AI agent to use this tool effectively. The 100% schema coverage helps but doesn't compensate for missing behavioral context.

    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 schema already documents all 7 parameters thoroughly. The description adds minimal value beyond the schema by mentioning 'optionally providing reference images' (implied by the images parameter) and the sibling tool reference. It doesn't provide additional parameter semantics beyond what's already in the structured 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?

    The description clearly states the tool's purpose with specific verbs ('Create a new image') and resources ('using Google Gemini AI from a text description'), and distinguishes it from its sibling tool ('Use the `edit_image` tool when you need to modify an existing asset'). This provides immediate clarity about what this tool does versus alternatives.

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

    Usage Guidelines5/5

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

    The description explicitly provides usage guidance by stating when to use this tool ('Create a new image') versus when to use the alternative ('Use the `edit_image` tool when you need to modify an existing asset'). This gives clear context for tool selection without ambiguity.

    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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