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mernorthzide

mrn-gemini-imagen-mcp

by mernorthzide
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, distinct purpose: generate creates new images, edit modifies existing ones with a single prompt, and iterate enables multi-turn refinement. The descriptions make the boundaries unambiguous.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (generate_image, edit_image, iterate_image), making them easy to understand and predict.

    Tool Count5/5

    Three tools is an ideal size for this domain—covering creation, editing, and iterative refinement without redundancy or unnecessary bloat.

    Completeness5/5

    The tool surface fully covers the core image manipulation workflow: generate from scratch, edit with a prompt, and iteratively refine through conversation. No obvious gaps for the intended purpose.

  • Average 3.6/5 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

  • 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

  • Behavior2/5

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

    No annotations exist, so description must cover behavioral traits. It mentions the model and return value but omits details like potential costs, rate limits, side effects (e.g., storage usage), or any safety/ethical considerations.

    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?

    Extremely concise single sentence. Every word adds value, and the most important information (action, model, output) is front-loaded.

    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 no output schema, the description adequately states the return value (file path). For 3 parameters and a straightforward generation task, this is sufficient, though additional details like supported file formats could further improve completeness.

    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 baseline is 3. The description adds no extra meaning beyond what the schema already provides for each parameter. The 'outputPath' and 'aspectRatio' are adequately described 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?

    The description clearly states the action (generate), resource (image), input source (text description), model (Google Gemini), and output (file path). It implicitly distinguishes from siblings 'edit_image' and 'iterate_image' by focusing on generation from description.

    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 on when to use this tool versus alternatives like 'edit_image' or 'iterate_image'. No context on prerequisites or scenarios where this tool is preferred.

    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 exist, so the description must disclose behavioral traits. It does not explain what happens during iteration (e.g., state management, side effects, whether the image is modified in place or returned). Key behaviors like output format and session lifecycle are missing.

    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?

    Two concise sentences that front-load the purpose. No redundant information; every sentence adds value.

    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?

    For a complex tool with 4 parameters and no output schema, the description is insufficient. It lacks details on return values, session behavior, and how iterations affect previous state. Agent would be uncertain about what to expect.

    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 additional meaning beyond the parameter descriptions in the schema. It does not explain how parameters interact or provide examples.

    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: iterative image refinement through multi-turn conversation. It distinguishes between starting a new session and continuing an existing one, and the name 'iterate_image' contrasts with siblings 'edit_image' (single edit) and 'generate_image' (creation from scratch).

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

    Usage Guidelines3/5

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

    The description provides usage context (start new or continue session) but does not explicitly advise when to use this tool versus alternatives like 'edit_image' or 'generate_image'. No guidance on prerequisites or when not to use.

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

  • Behavior3/5

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

    With no annotations, the description must disclose behaviors. It mentions using Gemini and that it preserves other parts, but does not clarify if edits are destructive, file format support, or whether outputPath is needed to avoid overwriting.

    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?

    Two concise sentences: first defines purpose, second provides a usage hint. No wasted words.

    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?

    Lacks details on output behavior (e.g., where the edited image is saved, file format), prerequisites (image must exist), and error handling. For a file-modifying tool, this is insufficient without annotations.

    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 descriptions already explain parameters. The tool description does not add extra meaning beyond the schema, meeting the baseline for high coverage.

    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 edits an existing image using a text prompt with Google Gemini. It specifies the resource (existing image) and the action (edit), distinguishing it from siblings generate_image and iterate_image.

    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 a clear use case: modifying specific parts while preserving others. Implicitly distinguishes from generate_image (creation) and iterate_image (likely iteration), but does not explicitly state when to avoid using it.

    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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