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falahgs

image-gen3-google-mcp-server

by falahgs
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a completely different operation: generate_images creates new images via AI, while create_image_html outputs HTML for existing image files. There is no ambiguity or overlap between them.

    Naming Consistency5/5

    Both tools follow a clear verb_noun pattern in snake_case. 'generate_images' and 'create_image_html' use consistent styling and predictable naming conventions.

    Tool Count3/5

    Only 2 tools for an image generation service is borderline. While the core functionality (generation and HTML output) is covered, the small number suggests a limited scope that may require additional tools for a complete workflow.

    Completeness2/5

    The tool set is notably incomplete: it lacks tools for listing, deleting, or managing generated images, and there is no way to configure generation parameters beyond what might be in the description. Users are left with a generation-and-output loop without lifecycle management.

  • Average 3/5 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

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  • 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

  • Behavior1/5

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

    With no annotations, the description should disclose behavioral traits but does not. It omits details about error handling, output format, or side effects, leaving the agent uninformed.

    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 with no waste. It is concise but could be slightly more informative without sacrificing brevity.

    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?

    For a simple tool with 3 parameters and no output schema, the description gives the basic idea but lacks details such as tag format, handling of missing files, or default attributes. It is adequate but not thorough.

    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. The description adds no extra parameter information beyond what the schema already provides.

    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 it creates HTML img tags from file paths. It distinguishes from sibling tool generate_images by focusing on HTML output rather than image 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 the sibling generate_images. No context on prerequisites or appropriate scenarios is given.

    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, the description carries full responsibility for behavioral disclosure. It only states that images are generated, but omits key traits such as whether the tool saves files to disk (though implied by 'outputDir' in schema), cost implications, rate limits, or error behavior. The agent learns little beyond the basic purpose.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single sentence achieving conciseness, but it lacks structure and front-loading of critical constraints. It does not highlight the image count range (1-4) or output directory default, which are important for usage but are buried in the schema.

    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?

    The tool has no output schema, so the description should explain what the tool returns (e.g., file paths, base64 data). It does not. Given the sibling tool suggesting alternative image-related functionality, more context on output format and typical use cases would be beneficial.

    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 the schema already documents all parameters. The description adds the context of using 'Google Gemini AI', which is not in the schema. However, it does not clarify or enrich the parameter meanings beyond the schema descriptions. Baseline of 3 is appropriate.

    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 action ('generate images') and specifies the service ('Google Gemini AI'). It effectively distinguishes from the sibling 'create_image_html' tool by indicating this generates actual images rather than HTML.

    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 the sibling 'create_image_html'. There are no conditions, prerequisites, or exclusions mentioned, leaving the agent to infer context from the tool name alone.

    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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