Skip to main content
Glama
leonaries

AI Picture MCP Server

by leonaries
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.4

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined for generating AI images for web design.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'generate_web_image' follows a clear verb_noun pattern.

    Tool Count2/5

    One tool is too few for a server with a broad purpose like 'AI Picture MCP Server', which suggests capabilities beyond just web image generation. This minimal toolset limits functionality and feels incomplete for the implied scope.

    Completeness2/5

    The server's name implies a general AI picture domain, but the single tool only covers web image generation. There are significant gaps, such as lack of tools for other image types (e.g., general-purpose, editing, or analysis), making the surface severely incomplete.

  • Average 3.1/5 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
    • 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.

  • 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

  • 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. It mentions the API (DashScope) and model (FLUX) but fails to disclose critical traits like rate limits, costs, authentication requirements beyond the apiKey parameter, error handling, or output format. This leaves significant gaps for an AI agent to understand operational constraints.

    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 concise and well-structured in two sentences: the first states the core purpose, and the second provides usage examples. Every sentence adds value without redundancy, making it efficient and front-loaded with key information.

    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 (AI image generation with 5 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain the return value (e.g., image file, URL, or error details), performance expectations, or limitations, leaving the agent with insufficient context to use the tool effectively.

    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% description coverage, documenting all 5 parameters thoroughly. The description adds no additional parameter semantics beyond what the schema provides, such as explaining relationships between parameters or usage nuances. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

    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: 'Generate AI images for web design using FLUX model via DashScope API.' It specifies the action (generate), resource (AI images), and context (web design). However, with no sibling tools, it cannot demonstrate differentiation from alternatives, preventing a perfect score.

    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 implied usage context by listing examples ('placeholder images, hero images, product images, and other web assets'), which suggests when to use it. However, it lacks explicit guidance on when not to use it or alternatives, and there are no sibling tools to compare against, so the guidance is incomplete.

    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

aipic-mcp MCP server

Copy to your README.md:

Score Badge

aipic-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/leonaries/aipic-mcp'

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