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dev66613

openrouter-image-gen-mcp

by dev66613
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    generate_image and list_models serve clearly different purposes: one creates an image, the other retrieves model metadata. There is no overlap or ambiguity between the two tools.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern using snake_case, making the naming predictable and consistent across the set.

    Tool Count3/5

    Two tools is on the thin side for an image generation server, but the pair covers the essential generation action and a supporting model lookup. It feels minimal yet not unreasonable.

    Completeness4/5

    The core image generation workflow is covered by generate_image, and list_models provides useful context for model selection. Minor gaps exist, such as no ability to inspect generation parameters or retrieve past generations, but these are not critical for a simple image generation API.

  • 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 is passing

  • This repository is licensed under Do What The F*ck You Want To Public 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 are provided, so the description carries the full burden of behavioral disclosure. 'Show information' implies a read-only operation, but it does not disclose what data is returned, whether a remote API is called, or whether any rate limits or prerequisites apply.

    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 short sentence with no filler. It loses one point because 'Show information about' is somewhat vague and 'model' is singular, while the tool name list_models suggests listing multiple models.

    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?

    Despite being a zero-parameter tool, there is no output schema, no annotations, and no description of what information will be shown or how this tool relates to generate_image. An agent can guess the basic behavior, but important selection and return-value context is missing.

    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?

    The input schema has zero properties and schema coverage is 100%, so there are no parameter semantics to document. The description cannot add meaning to an already complete empty 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?

    The description uses a clear verb-resource pair: 'Show information' about 'the Gemini image generation model.' It is distinguishable from the sibling generate_image because one provides model information and the other generates images, though it does not explicitly name or contrast the sibling.

    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 given about when to call this tool instead of generate_image. The only implied context is that listing model information might precede generation, but the description does not state this or any other selection criteria.

    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 must carry the full behavioral burden. It states that images are generated via Gemini API but does not disclose what the tool returns by default, whether it saves files, whether authentication or costs are involved, or any side effects. The schema hints at save_to_file and show_full_response, but the description itself adds little behavioral context.

    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 with no wasted words. The primary action is front-loaded, and the second sentence gives practical prompt-construction guidance without bloating the description.

    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 tool with four parameters fully documented in the schema, the description covers the core invocation guidance. However, without annotations or an output schema, it leaves gaps around default return behavior, file-saving semantics, and operational prerequisites such as API access.

    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 four parameters. The description reinforces the prompt guidance about style, aspect ratio, and composition, but it does not add meaning 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 opens with a specific verb and resource: 'Generate images using Google Gemini API.' It clearly differentiates from the sibling tool list_models, which serves a different purpose, and adds useful guidance that style, aspect ratio, and composition can be controlled through the prompt.

    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?

    The description clearly implies the tool is for generating images, so an agent can infer when to call it. It does not explicitly discuss when not to use it, but no competing image-generation sibling exists among the listed tools, so the omission is minor.

    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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