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crawde

mcp-xmorf

by crawde
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    Each tool has a distinct purpose: generating images, editing images, and listing models. No ambiguity between them.

    Naming Consistency5/5

    All tool names follow a consistent pattern: xmorf_verb_noun (edit_image, generate_image, list_models).

    Tool Count5/5

    Three tools cover the core functionality (generate, edit, model info) without unnecessary clutter, well-scoped for the server's purpose.

    Completeness4/5

    The set covers the main operations (generation, editing, model listing). A minor gap could be a tool for deleting or retrieving generated images, but the core workflow is complete.

  • Average 3.6/5 across 3 of 3 tools scored. Lowest: 3/5.

    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.

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

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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 provided. Description only says 'using AI' without disclosing specifics like API provider (OpenAI), authentication, rate limits, or output handling. Behavioral traits such as cost implications or generation time are missing.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

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

    Description is a single sentence, very concise. However, it lacks structured details and is arguably too brief for a tool with 4 parameters. Could be improved with bullet points or additional context.

    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?

    With 4 parameters, no output schema, and no annotations, the description is insufficient. It omits details like output format (base64 or file), authentication, model defaults, and error handling. The tool's complexity demands more context.

    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% (all 4 parameters documented). The description adds no extra meaning beyond the schema. Baseline score is appropriate.

    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 an image from a text description using AI'. It specifies the action (generate), resource (image), and input (text description). Siblings (xmorf_edit_image, xmorf_list_models) have different purposes, so this tool is well-differentiated.

    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 on when to use this tool versus siblings (e.g., when to generate vs edit vs list models). No when-not-to-use or prerequisite information. The description is a generic statement without usage context.

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

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description bears full responsibility for behavioral disclosure. It explains the input format (file path or base64) and several model behaviors, but omits details on side effects, authentication needs, rate limits, return format (base64 vs. file save), or limitations (e.g., file size constraints).

    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 sentences: first sentence states purpose, second lists models and input formats. No redundant or empty phrases. The information is front-loaded and efficiently packed.

    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?

    No output schema is provided, so the description should clarify the return value (base64 image data or file saved). This is missing. Additionally, it lacks guidance on image size limits, required permissions, or error handling. For a tool with 5 parameters and no annotations, the description is incomplete.

    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?

    Input schema coverage is 100%, so baseline is 3. The description adds value by clarifying that the 'image' parameter can be a file path or base64 data URL, and it explains the purpose of each model value beyond the enum labels, e.g., 'shadow (light migration, needs reference)'.

    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 'Edit an image using AI with a natural language prompt', which is a specific verb and resource. The list of models adds detail but does not explicitly differentiate from sibling tools 'xmorf_generate_image' and 'xmorf_list_models', though the edit vs generate distinction is implicit.

    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 lists models with brief purposes (e.g., 'enhance (realism)', 'shadow (light migration, needs reference)'), providing some guidance on when to use each. However, it does not explicitly contrast with the 'generate' sibling (which creates new images) or provide exclusions or prerequisites.

    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?

    No annotations are provided, so the description carries the full burden. It accurately describes a read-only operation (listing models) with no hidden side effects. For a simple list tool, this is adequate and transparent.

    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, front-loaded sentence that efficiently conveys the tool's purpose without unnecessary words. Every word earns its place.

    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 has no parameters or output schema, the description is complete. It tells an agent everything needed to understand and invoke the tool: it lists models and descriptions.

    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 tool has no parameters, and the schema is fully covered (100%). By baseline for 0 parameters, a score of 4 is appropriate since no additional parameter description is needed.

    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 the tool lists available xmorf image editing models and their descriptions, using a specific verb and resource. It distinguishes from sibling tools (xmorf_edit_image and xmorf_generate_image) which focus on editing and generating images, not listing models.

    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?

    While the purpose is clear and sibling tools imply different contexts, the description does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or prerequisites. Usage is implied but not guided.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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