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fl0w1nd

Grok Image MCP

by fl0w1nd
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TypeScript
Hybrid

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one generates new images from text, the other edits existing images. There is no overlap or confusion.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern in snake_case (edit_image, generate_image), making the naming predictable and clear.

    Tool Count4/5

    With only two tools, the server is minimal but covers the core operations of image generation and editing. The count is slightly low but reasonable for a focused purpose.

    Completeness5/5

    The server provides the essential operations for an image generation and editing tool: creating new images from prompts and editing existing images. No obvious gaps are present for its stated purpose.

  • Average 4/5 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 is passing

  • 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

  • Behavior4/5

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

    With no annotations, the description carries the full burden. It discloses output format (image URLs in markdown) and the critical fact that URLs are temporary. This is valuable behavioral context. However, it omits details like rate limits, cost, or potential failure modes.

    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 two sentences, each earning its place: the first defines core functionality, the second warns about temporary URLs. No redundant or extraneous information. Front-loaded and efficient.

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

    Completeness3/5

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

    Given no output schema, the description could explain more about the return format (e.g., markdown syntax, array vs single URL, example). It also lacks details on generation style or quality. While it covers the essential purpose and a key warning, it is not fully complete for a tool with 4 parameters and no output schema.

    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 have descriptions). The description adds no extra meaning beyond the schema; it merely restates 'text prompts' which aligns with the prompt parameter. Therefore, baseline score of 3 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?

    The description clearly states the tool generates images from text prompts using the Grok image model, with a specific verb and resource. It distinguishes from the sibling tool 'edit_image' which presumably edits images, so purpose is unambiguous.

    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 post-generation guidance (URLs are temporary, download promptly) but does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it. The guidance is helpful but lacks selection 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 provides basic behavioral info: input formats (URLs, base64, file paths) and output format (markdown URLs). However, it lacks disclosure of potential side effects (e.g., whether originals are modified), rate limits, authentication requirements, or error handling. The behavior beyond input/output is opaque.

    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 consists of two concise sentences. The first states the core purpose, the second details input requirements and output format. Every word contributes, no redundancy, and critical information is front-loaded.

    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 5 parameters with full schema descriptions, the description adds context for image input formats and output representation. It does not detail default behaviors for optional parameters (n, aspect_ratio, resolution), but those are covered by the schema. The lack of an output schema is partially addressed by the markdown hint.

    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 the baseline is 3. The description adds value by clarifying that image_urls can be URLs, base64 data URIs, or local file paths, and that the output is in markdown format—details not fully explicit in the schema parameter descriptions themselves.

    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 starts with 'Edit existing images using a text prompt', clearly specifying the verb (edit), resource (existing images), and method (text prompt). It explicitly mentions providing source images and editing instructions, distinguishing it from the sibling tool 'generate_image' which creates new images.

    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 implicitly indicates usage for editing existing images by requiring 1-3 source images, but it does not explicitly state when to use this tool versus the sibling 'generate_image', nor does it provide exclusions or limitations. No guidance on prerequisites or failure scenarios.

    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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