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JimothySnicket

gemini-image-mcp

by JimothySnicket
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Server Quality Checklist

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

  • Latest release: v0.5.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: generate_image handles AI-powered generation/editing via Google Gemini, while process_image performs local image operations using sharp. Descriptions explicitly clarify when to use each, leaving no ambiguity.

    Naming Consistency5/5

    Both tool names follow a consistent 'verb_noun' pattern: 'generate_image' and 'process_image'. This pattern is uniform and predictable, making it easy for an agent to infer functionality.

    Tool Count4/5

    With only 2 tools, the server is minimalist but well-scoped for its purpose of image generation and processing. While more tools could be added (e.g., for metadata extraction), the current count is reasonable for a focused MCP server.

    Completeness4/5

    The tools cover the core image lifecycle: AI generation/editing and local processing (crop, resize, background removal, format conversion). Minor gaps exist (e.g., no direct download or metadata retrieval), but the essential workflows are supported.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • No commit activity data available
    • 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.

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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?

    No annotations provided, so description carries full burden. It discloses return values (saved file path, model, tokens, cost), removeBackground behavior (first-use model download, fallbacks, mode effects), and runtime validation. Missing auth/rate limits, but still transparent for a complex tool.

    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?

    Description is relatively long but well-structured: starts with core purpose, then specific capabilities (removeBackground), then parameter details. Each sentence adds value. Slightly verbose in removeBackground section, but overall efficient.

    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?

    Given 12 parameters, nested object, no output schema, and 100% schema coverage, the description is thorough. It explains return values and parameter interactions (e.g., seed reproducibility, session continuation, model support). Could add error handling details, but still highly complete.

    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 description coverage is 100%, baseline 3. Description adds significant value: explains sessionId for multi-turn edits, removeBackground sub-parameters (auto, chroma, threshold) with defaults and dependencies, and default resolution/aspect-ratio derivation. This goes well beyond the schema.

    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?

    Description clearly states tool generates or edits images using Google Gemini, listing two main modes: text-to-image and editing with reference images. The sibling tool process_image is not differentiated explicitly, but the description's focus on generation/editing implies separation.

    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?

    Provides clear guidance: 'Provide just a prompt for text-to-image generation. Add image file paths to edit or use reference images.' Also gives detailed usage instructions for removeBackground modes (auto, chroma, threshold) with pros and cons. Lacks explicit exclusions or when-not-to-use, but context is sufficient.

    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?

    No annotations provided, so description carries full burden. It discloses key traits: local execution, free, fast, no API calls. However, it does not mention potential side effects, file system modifications, or return behavior in detail.

    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 sentences, front-loaded with main action, then operations list, then alternative guidance. No redundancy or unnecessary words.

    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?

    Given the complexity (10 parameters, nested objects, no output schema), the description covers core actions and context but omits explicit mention of output (e.g., saved file path). The output parameters imply saving, but it's not stated.

    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%, so baseline is 3. Description adds a high-level summary of operations but does not significantly augment the detailed parameter descriptions already in 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?

    Clearly states the tool processes existing images locally using sharp, enumerates specific operations (crop, resize, remove background, etc.), and distinguishes from sibling tool generate_image by noting when to use that alternative.

    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?

    Explicitly advises to use generate_image for AI-powered editing, providing clear when-to-use guidance for this tool versus its sibling.

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

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