Skip to main content
Glama
12-days-of-shipmas-2025

Image Generation MCP

by 12-days-of-shipmas-2025
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.1.1

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one generates images with various format options, the other lists available formats. No overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (generate_blog_image, list_image_formats), making it easy to understand their function at a glance.

    Tool Count3/5

    With only two tools, the server feels slightly thin for its domain. While the generation tool is comprehensive, a few additional tools (e.g., for image editing or batch generation) could better match the scope.

    Completeness5/5

    The server's stated purpose is generating images for blogs and social media, and listing formats. The generation tool supports a wide variety of formats and options, and the format listing tool provides necessary metadata. No critical gaps are apparent.

  • Average 4.1/5 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
    • 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.

  • 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.

  • Add a glama.json file to provide metadata about your server.

  • If you are the author, simply .

    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    With no annotations, the description carries full burden. It clearly indicates a read-only listing operation and describes the content of the output (dimensions and use cases). It does not discuss permissions or side effects, but for a simple listing tool, this is sufficient.

    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, concise sentence that front-loads the action ('List all...') and specifies what the output contains. Every word adds value, and there is no redundancy or filler.

    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 low complexity (1 optional parameter, no output schema), the description adequately covers the tool's purpose and output content. It could explicitly state that all presets are returned when no category is provided, but the word 'all' implies that. Sibling context is clear.

    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 input schema has 100% coverage with descriptions for the 'category' parameter, including an enum. The description does not add additional meaning to the parameter beyond what the schema provides, so baseline score of 3 is appropriate.

    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 lists all available image format presets and what they include (dimensions and use cases). It is distinct from the sibling 'generate_blog_image' which likely generates an image. However, it does not explicitly differentiate from siblings, missing a chance for clarity.

    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 implies the tool is used to view available presets before generating an image, but provides no explicit guidance on when to use or not use it, nor any alternatives. The context of a single sibling suggests usage, but lacks direct instruction.

    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?

    With no annotations, description discloses default save behavior and outputPath importance. Does not mention auth, rate limits, or side effects beyond file creation, but cover key behavioral aspects adequately.

    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?

    Well-organized with headings, bullet list, examples, and a note. Every section serves a purpose, though the format list could be condensed. Front-loaded with purpose, then details.

    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?

    For a 7-param tool with no output schema or annotations, the description explains all inputs, default behaviors, and provides examples. Lacks return value details but that falls on schema.

    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 coverage is 100%, baseline 3. Description adds dimensions to each format, examples for quality/title, and clarifies outputPath purpose. Adds meaningful context beyond schema definitions.

    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?

    Description clearly states 'Generate images for blog posts and social media using AI' - a specific verb and resource. Sibling tool 'list_image_formats' lists formats, while this generates images, making the distinction obvious.

    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?

    Examples show typical use cases and the IMPORTANT note guides outputPath usage. Lacks explicit when-not-to-use or alternative tools, but the sibling count is small and context is clear.

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

GitHub Badge

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.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

day-2-image-generation-mcp MCP server

Copy to your README.md:

Score Badge

day-2-image-generation-mcp MCP server

Copy to your README.md:

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/12-days-of-shipmas-2025/day-2-image-generation-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server