Skip to main content
Glama
alexlivre

minimax-image-mcp

by alexlivre
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Local

Server Quality Checklist

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

  • Latest release: v1.0.3

  • Disambiguation5/5

    With only one tool, there is no risk of confusion or overlap between tools. The single tool's purpose is clearly defined.

    Naming Consistency5/5

    The tool name 'minimax_image_generate' follows a clear verb_noun pattern. Since there is only one tool, consistency is trivially maintained.

    Tool Count2/5

    The server provides only one tool for image generation, which is insufficient for a comprehensive service. Typical image generation servers include additional tools for configuration, model management, or image handling, making this feel extremely thin.

    Completeness2/5

    The single tool attempts to cover image generation and image-to-image, but lacks supporting operations such as model listing, parameter validation, or result management. The surface is incomplete for a robust image generation workflow.

  • Average 4.4/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 21 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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?

    Beyond annotations (which are not very informative), the description discloses that images are saved to disk and file paths returned, and that prompt optimization may add extra elements. It does not cover rate limits or potential side effects, but adds useful 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.

    Conciseness4/5

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

    The description is a single paragraph covering all key features. It is fairly concise but could be slightly more structured (e.g., bullet points) for easier scanning. Still, it is efficient and well written.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool complexity (8 parameters, 100% schema coverage, multiple features), and the presence of an output schema, the description is complete. It mentions file path returns and covers all major capabilities.

    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%, so baseline is 3. The description adds value by explaining the speed benefit of n=9, expiry difference between 'url' and 'base64', and the effect of prompt_optimizer. This goes beyond the 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?

    The description clearly states it generates images from text prompts using a specific model, and enumerates supported features (aspect ratios, batch generation, etc.). It is specific and distinguishes the tool from any potential alternatives.

    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 provides context like recommending n=9 for speed, but does not explicitly state when to use this tool versus alternatives. Since no siblings are listed, it is still clear, but lacks explicit when-not guidance.

    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

minimax-image-mcp MCP server

Copy to your README.md:

Score Badge

minimax-image-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/alexlivre/minimax-image-mcp'

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