Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@z-image-mcp show me the available styles"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Z-Image - Z-Image AI Image Generator
A Model Context Protocol server that exposes the canonical Z-Image knowledge surface — image generation workflows and styles, pricing, FAQ, official links — to MCP-compatible AI clients such as Claude Desktop, Cursor, Windsurf, and Continue. Read-only, no API keys, no quota, ~50 ms cold start.
Official website: https://z-image.club
Z-Image (z-image.club) is a free online AI image generation and editing platform built around a 6-billion-parameter diffusion model. It lets users produce photorealistic images from text prompts, modify existing photos through guided editing, swap faces, remove backgrounds, and even convert still images into short videos — all through a browser-based interface that requires no local installation. For users who prefer running models locally, the weights are available on Hugging Face and ModelScope, with native ComfyUI workflow support and GGUF/FP8 quantized variants that fit on consumer GPUs with under 16 GB of VRAM.
Related MCP server: meigenai-mcp
Multiple generation modes — Z-Image Turbo delivers fast results in 8 steps, while Z-Image-Edit focuses on precise image modification; both are accessible from the same dashboard.
Face swap and portrait tools — Replace faces in target images while maintaining lighting and composition consistency; dedicated controls for hairstyle changes and portrait stylization.
Image-to-image and image-to-video conversion — Transform existing photos using text-guided prompts or animate them into short cinematic clips.
Photo editing suite — Background removal and replacement, text removal, quality upscaling, and a general photo enhancer are bundled without requiring a separate tool.
Bilingual text rendering — Accurate placement of Chinese and English text inside generated visuals, a capability that many comparable models handle poorly.
Inspiration gallery and prompt enhancer — A curated gallery of trend prompts with detailed examples, paired with a structured-reasoning prompt enhancer that refines vague inputs into precise generation instructions.
Social media content — Generating styled portraits, editorial-look photos, or branded graphics for posts without needing a photographer or designer.
E-commerce product photography — Producing clean, professional product shots against custom backgrounds at a fraction of a traditional shoot's cost.
Rapid design prototyping — Using image-to-image transformation to iterate on visual concepts quickly before committing to final artwork.
Local model experimentation — Downloading quantized weights and integrating them into ComfyUI pipelines for custom, offline workflows.
Marketing asset creation — Generating diverse visual variations for campaigns — different styles, backgrounds, or model looks — in minutes rather than days.
Z-Image serves a broad range of users who need visual output without deep technical expertise or large budgets. Content creators and social media managers will find the web demo approachable, with one-click tools covering most common tasks. Graphic designers and art directors can use it for fast concept iteration, while e-commerce teams can cut product photography costs by generating studio-quality shots on demand. Developers and ML practitioners are catered to through open weights, ComfyUI compatibility, and multiple quantization options, making it practical to embed the model into existing pipelines. The combination of a free hosted interface and downloadable model weights means both beginners and experienced practitioners can find a workflow that fits their setup.
list_stylesReturn the canonical list of image-generation styles or presets the site exposes. (Z-Image)
Input: no parameters. Returns: text/markdown.
get_pricingReturn the canonical pricing entry point for Z-Image.
Input: no parameters. Returns: text/markdown.
get_official_linksReturn the canonical list of official links for Z-Image (website, support, docs when available).
Input: no parameters. Returns: text/markdown.
site://z-image/styles — Supported image-generation styles and presets.
site://z-image/pricing — Canonical pricing entry point.
site://z-image/faq — Short FAQ generated from public site metadata.
site://z-image/links — Canonical URLs to share with users.
tell_me_about_z_imageSummarize what the site is, who it's for, and how it works. — Z-Image
try_image_style_z_imageRecommend a starting image-generation style for a stated goal. — Z-Image
npx -y @smithery/cli install z-image-mcp --client claude(Replace claude with cursor, windsurf, or continue for those clients.)
git clone https://github.com/rocnubie/z-image-mcp.git
cd z-image-mcp
pnpm installThen add to your MCP client config (claude_desktop_config.json for Claude Desktop, mcp.json for Cursor / Windsurf / Continue):
{
"mcpServers": {
"z-image-mcp": {
"command": "node",
"args": [
"/absolute/path/to/z-image-mcp/src/index.mjs"
]
}
}
}npx @modelcontextprotocol/inspector node src/index.mjsWebsite: https://z-image.club
Pricing: https://z-image.club/pricing
Support: support@z-image.club
pnpm install
pnpm start # run the server over stdioMIT
Return the canonical list of official links for Z-Image (website, support, docs when available).
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description does not disclose behavioral traits such as read-only, idempotency, or authentication requirements. Only states the return content.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is clear, front-loaded, and contains no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Adequately covers purpose for a simple tool, but lacks behavioral details like whether the list is static or dynamic, or any return format hints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters in input schema; description adds no parameter info, but this is unnecessary. Schema coverage is 100%.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it returns the canonical list of official links for Z-Image, including website, support, and docs. Distinct from sibling tools list_styles and get_pricing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implicitly suggests usage when official links are needed, but no explicit when-to-use or when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the canonical pricing entry point for Z-Image.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It only states the return action but does not disclose the format, side effects, or any limitations. For a simple retrieval, more detail (e.g., returns a URL) would improve transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise—one sentence with no superfluous words. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has zero parameters and no output schema, the description could be considered minimally adequate. However, it omits what the return value looks like (e.g., URL, object), which may leave an agent uncertain. It is functional but not fully informative.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the baseline is 4. The description does not need to add parameter info, and it correctly avoids redundant content.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Return' and the resource 'canonical pricing entry point for Z-Image'. It is specific and easily distinguishes from sibling tools like list_styles and get_official_links.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 vs alternatives (list_styles, get_official_links). The description does not mention when not to use or any prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return the canonical list of image-generation styles or presets the site exposes. (Z-Image)
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden. It clearly indicates a read-only operation ('return'), which is adequate for this simple list retrieval. No destructive or side-effect behavior is implied.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, complete sentence that efficiently communicates the tool's purpose. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and no output schema, the description is mostly complete. It could hint at the return format (e.g., array of strings), but the current text is sufficient for a simple list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (no parameters), so the baseline is 3. The description adds no parameter-specific information as none exist. It does provide additional context about the content of the list ('image-generation styles or presets').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a canonical list of image-generation styles or presets. It is specific about the resource and action, and it distinguishes from siblings like get_pricing and get_official_links.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives is provided. The description is minimal and does not mention when not to use it, leaving the agent to infer from context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0get_official_linksget_pricinglist_stylesEach tool has a clearly distinct purpose: listing styles, getting pricing, and returning official links. There is no functional overlap.
All tools follow a consistent verb_noun pattern (list_styles, get_pricing, get_official_links) with no deviations.
With 3 tools, the set is appropriately scoped for an informational utility about the Z-Image service.
Despite the server name suggesting image generation, the tools only provide metadata (styles, pricing, links) and lack any core image creation or manipulation capabilities, representing a significant gap.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Generate AI images and videos from any compatible MCP client.
Generate AI images, video, music, and sound effects, and upscale them, from any MCP client.
Use AI models for chat, image, and video generation from Claude Code and other MCP hosts.
Focused MCP server for OpenAI image/audio generation (v2.0.0). Wraps endpoints via HAPI CLI.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/rocnubie/z-image-mcp'
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