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Sunwood-ai-labs

Ideogram MCP Server

by Sunwood-ai-labs
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TypeScript
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The single tool 'generate_image' has a clearly distinct and unambiguous purpose.

    Naming Consistency5/5

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

    Tool Count2/5

    One tool is too few for a server named 'Ideogram MCP Server', which suggests a broader scope for image generation or AI tasks. A single tool feels thin and limited for such a domain.

    Completeness2/5

    The tool surface is severely incomplete for an image generation server. It only offers generation with no options for editing, listing, deleting, or managing images, creating significant gaps in functionality.

  • Average 2.7/5 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
    • 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.

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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 full burden for behavioral disclosure. The description only states the basic action without mentioning rate limits, authentication needs, output format, error conditions, or cost implications. For a complex image generation tool with 15 parameters, this leaves significant behavioral gaps.

    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, efficient sentence that states the core purpose without unnecessary elaboration. It's appropriately sized for a tool name that clearly indicates its function, and there's no wasted verbiage or structural issues.

    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 (15 parameters, no output schema, no annotations), the description is inadequate. It doesn't explain what the tool returns, error handling, performance characteristics, or typical use patterns. For an image generation tool with many configuration options, more context is needed to help the agent use it 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?

    Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no parameter information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose3/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Generate an image using Ideogram AI' states the basic action (generate) and resource (image) but lacks specificity. It doesn't mention what kind of images, quality levels, or typical use cases. Without sibling tools, differentiation isn't needed, but the purpose remains vague beyond the basic verb-noun pairing.

    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 provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, ideal scenarios, or limitations. Without sibling tools, there's no need for differentiation, but the absence of any usage context leaves the agent with no guidance on appropriate application.

    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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  • 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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