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creating-cat

gemini-image-mcp

by creating-cat
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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

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'generate_image' has a clearly distinct and singular purpose, making it impossible for an agent to misselect between non-existent alternatives.

    Naming Consistency5/5

    The single tool name 'generate_image' follows a clear verb_noun pattern (generate + image), and with only one tool, there is no inconsistency or deviation to evaluate. The naming is straightforward and predictable.

    Tool Count2/5

    A single tool is too few for most server purposes, as it limits functionality and flexibility. For an image generation server, one tool feels thin and under-scoped, lacking operations like editing, listing, or deleting images, which could hinder agent workflows.

    Completeness2/5

    The tool surface is severely incomplete for an image generation domain. While 'generate_image' covers creation, there are obvious gaps such as no ability to retrieve, update, delete, or manage generated images, leading to potential dead ends in agent tasks.

  • Average 2.9/5 across 1 of 1 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
    • 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.

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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 mentions saving the image but omits critical details like potential side effects (e.g., file overwriting), performance considerations (e.g., generation time, resource usage), error handling, or output specifics. For a complex tool with 12 parameters, this lack of behavioral context 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, efficient sentence that directly states the tool's core function without unnecessary words. It is front-loaded with the essential action, making it highly concise and well-structured for quick understanding.

    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 (12 parameters, no output schema, and no annotations), the description is inadequate. It fails to explain behavioral traits, output details, or usage context, leaving the agent with insufficient information to effectively invoke the tool beyond basic parameter filling.

    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?

    The schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no additional parameter semantics beyond implying a 'prompt' and 'path' are involved, which is already covered. Thus, it meets the baseline of 3 where the schema does the heavy lifting without adding extra value.

    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: 'Generates an image based on a prompt and saves it to the specified path.' It specifies the verb ('generates'), resource ('image'), and destination ('saves it to the specified path'), making the action explicit. However, it doesn't differentiate from siblings since there are none, so it cannot achieve a perfect score of 5.

    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, prerequisites, or contextual constraints. It merely states what the tool does without indicating scenarios for its application, leaving the agent without usage direction.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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