Stability AI MCP. Generate, refine, and clean professional visual assets instantly.
Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Stability AI MCP Server connects your agent directly to advanced visual models for image generation, upscaling, and precise editing. Generate photorealistic mockups from text prompts or clean product shots by removing complex backgrounds automatically.
Use core tools like `generate_sd35` and `remove_background` to handle entire design pipelines conversationally.
What your AI agents can do
Generate core v2
Generates an image using the Stable Image Core model, optimized for speed and quality.
Generate sd35
Creates images using Stable Diffusion 3.5, letting you choose between three distinct large models.
Generate ultra v2
Produces high-end photorealistic image assets ideal for final production use.
Create brand new images by providing a detailed text prompt and selecting a core model like generate_sd35.
Increase the size of an existing image while preserving fine detail using the upscale_image tool.
Remove complex, messy backgrounds from product photos instantly with the remove_background tool.
Target and replace isolated sections within an image using inpaint_image based on a text prompt.
Change the style or subject of an existing picture by providing it with both a new text prompt and an engine ID via image_to_image_v1.
Verify your account's current usage balance using get_credit_balance before running expensive operations.
Ask AI about this MCP
Supported MCP Clients
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Stability AI MCP Server: 10 Tools for Image Generation
These tools give your agent full control over the visual pipeline. Generate concepts, enhance quality, and refine assets with specific functions like background removal.
019d760cgenerate core v2
Generates an image using the Stable Image Core model, optimized for speed and quality.
019d760cgenerate sd35
Creates images using Stable Diffusion 3.5, letting you choose between three distinct large models.
019d760cgenerate ultra v2
Produces high-end photorealistic image assets ideal for final production use.
019d760cget credit balance
Retrieves your current Stability AI credit balance to prevent unexpected usage fees.
019d760cimage to image v1
Transforms an existing image based on a new text prompt, requiring both the engine ID and the source picture.
019d760cinpaint image
Edits specific, isolated regions within an image by using a focused text prompt to guide the change.
019d760clist engines
Lists all available image generation engines and their IDs necessary for v1 tools.
019d760cremove background
Automatically detects and removes the background from a given product or scene photo.
019d760ctext to image v1
Generates an image using v1 engines, requiring you to specify an engine ID, prompt, width, and height.
019d760cupscale image
Increases the resolution of an existing picture while maintaining structural detail and quality.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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Make Your AI Do More
Start with Stability AI, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
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- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Stability AI MCP Server connects your agent straight into powerhouse visual models. You don't have to jump between a bunch of different external programs just to handle images; you run the whole gig right here in your chat interface. When you need mockups, or if you gotta clean up some messy product photos, this thing handles it all conversationally.
Your agent talks directly to these tools. You tell it what you want—a specific style of image, a cropped background, or just higher resolution—and the server executes it. It manages everything from synthesizing brand new visuals based on detailed text prompts to fixing low-res assets and surgically removing backgrounds. Here's how you use the core functions:
Generating Images From Scratch: When you need an entirely new visual, you've got a few routes depending on what you want the final product to look like. You can hit up generate_sd35 if you wanna use Stable Diffusion 3.5; this lets you choose from three separate, massive models for different looks.
If speed and quality are your main concerns, fire up generate_core_v2. For assets that need to be absolutely perfect—like stuff going into final print production—use generate_ultra_v2; it's built for high-end photorealism. You can also use text_to_image_v1 with v1 engines, but remember you gotta specify the engine ID, along with your prompt, width, and height.
Improving Existing Photos: If a source image is fuzzy or too small, don't sweat it. Run upscale_image. This tool blows up the dimensions of an existing picture without losing structural detail—it keeps things sharp so there’s no pixelation headache. Want to change a photo's style or swap out a subject? You use image_to_image_v1.
It takes your original source image and transforms it based on a new text prompt, but you gotta provide both the engine ID and that source picture for it to work.
Pinpoint Editing: Sometimes you don't want to change the whole photo; you just need to tweak one specific corner. For that, inpaint_image is your move. It lets you target an isolated section within an image and guide the replacement with a focused text prompt. If you gotta clean up a product shot by getting rid of a messy backdrop, use remove_background.
This tool automatically finds and strips out complex backgrounds from any product or scene photo instantly.
Engine Management and Credits: Before running anything expensive, you should always check your balance with get_credit_balance to make sure you don't get hit with unexpected usage fees. If you’re using the v1 tools (text_to_image_v1, etc.), you might need to know which engines are available first; run list_engines to pull a list of all engine IDs needed for those v1 functions.
How Stability AI MCP Works
- 1 Connect the Stability AI MCP server to your agent and attach your API key in the MCP settings.
- 2 Prompt your agent with a multi-step request (e.g., 'Generate an image, then upscale it, and remove the background').
- 3 The agent sequences the calls using tools like
generate_sd35->upscale_image->remove_background, delivering the final output.
The bottom line is your agent handles the whole technical pipeline—from generating initial concepts to running final cleanup passes, all from a simple chat prompt.
Who Is Stability AI MCP For?
Anyone who works with visual media but hates switching between Photoshop, Midjourney, and an API console. Think E-commerce Ops Managers drowning in product photos, or Marketing Directors running ad campaigns that need dozens of variations this week.
You run hundreds of SKUs and hate manually cutting backgrounds. You use remove_background to standardize all catalog images in bulk.
You need 15 ad variations by end-of-day for a campaign launch. You prompt the agent using generate_sd35 and iterate on concepts until they nail the look.
You have rough sketches that need to become high-resolution mockups for stakeholders. You run the full pipeline: prompt -> generate -> upscale_image.
What Changes When You Connect
- Need to standardize 500 product shots? Run
remove_backgroundonce. Your agent handles the cleanup across your entire catalog, saving hours of manual Photoshop work. - Got a rough concept sketch? Use the full pipeline: generate an initial image with
generate_sd35, then useupscale_imageto make it print-ready quality. - Stop guessing which model works. You can test different generation methods—from core models (
generate_core_v2) to specialized versions (generate_ultra_v2)—to find the right look, all in one chat session. - You don't have to redraw everything. If you just need to swap out a product color or replace a logo, use
inpaint_imagefor surgical precision without losing background detail. - It keeps your budget clear. Before running a massive batch of images, check the cost first with
get_credit_balance. No surprises when you're pushing volume.
Real-World Use Cases
Launching a new product line requires consistent imagery.
The E-Commerce Manager has 20 prototype photos taken in different lighting. Instead of hiring retouchers, they ask their agent to run remove_background on all 20 images and then use upscale_image to ensure every final asset is 4K resolution for the website.
A marketing campaign needs highly varied ad visuals.
The Marketing Designer wants to test a new electric scooter in five different environments. They prompt the agent with 'Generate a futuristic scooter on a rainy city street' using generate_sd35. The agent quickly runs this concept across multiple prompts and outputs variations for A/B testing.
A graphic designer needs to fix an existing mockup.
The designer has a high-res mockup but the phone model shown is wrong. They use inpaint_image, masking out the old phone and prompting for 'latest flagship smartphone design', fixing the error without re-generating the entire scene.
A concept artist needs to elevate early drafts.
The artist provides a low-res, colored sketch of an architectural feature. They use text_to_image_v1 with a detailed prompt and then follow up by running upscale_image, converting the initial draft into a high-fidelity presentation asset.
The Tradeoffs
Trying to fix an image without knowing its quality.
Prompting 'fix this photo' and running inpaint_image on a tiny, blurry JPEG. The result will be unusable garbage because the source data is too low-quality.
→
Before editing or inpainting, always run upscale_image first to boost resolution. If that fails, start fresh by using generate_sd35 with better initial parameters.
Forgetting background cleanup on product shots.
Generating a perfect e-commerce image but leaving the original messy desk or studio backdrop visible.
→
Always follow your generation step by asking the agent to run remove_background. This guarantees a clean, isolated asset ready for final placement.
Treating all generators equally.
Using generate_core_v2 for a critical client deliverable when you needed maximum realism. The model might not hit the photorealism mark.
→
For final, high-stakes assets, use generate_ultra_v2. For quick brainstorming or volume work, stick with generate_sd35.
When It Fits, When It Doesn't
Use this MCP Server if your core need is visual media creation, modification, or enhancement. You need to go beyond simple text-to-image generation; you need control over resolution (upscale_image), composition (inpaint_image), and background removal (remove_background).
Don't use it if: 1) You only need to summarize a PDF (use an NLP tool). 2) You just want to write a blog post about art (use a pure LLM connector). 3) Your problem is organizational, not visual.
The key strength here is the ability to chain tools. For example, don't treat generate_sd35 and remove_background as separate commands; ask your agent for 'Generate X, then remove its background.' The server manages that complex sequence for you.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Stability AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Tackling product photos used to mean endless manual cleanup.
Today, getting a clean e-commerce shot is a three-step nightmare. You shoot the product (with its messy backdrop). Then you open Photoshop, manually select and delete the background. Finally, you have to resize and re-export it at the exact dimensions your website demands. It takes time, specific skills, and multiple tools.
With this MCP Server, you just send the photo and prompt the agent: 'Clean up the background from this product.' The server runs `remove_background` on the fly. You get a perfectly isolated PNG asset ready to drop into your CMS—no extra software needed.
Stability AI MCP Server: Get production-ready images in conversation.
The old way meant running separate commands for generation, then another process for upscaling, and a third tool just to clean the edges. You were always managing parameters (width/height multiples of 64) and keeping track of which output fed into the next input.
Now, you talk to your agent like a teammate. 'Generate a futuristic bike mockup, make it super high-res, and remove any visible ground clutter.' The server handles the sequence—`generate_sd35` followed by `upscale_image`, ending with cleanup—and delivers one final asset.
Common Questions About Stability AI MCP
How do I get high-resolution images using generate_sd35? +
You specify the desired resolution and model parameters in your prompt. For maximum quality, follow up the generation with upscale_image to boost the dimensions further.
Does remove_background work on complex scenes or just products? +
It works on any image, but it excels at product photography. It isolates subjects cleanly by detecting and removing non-subject backgrounds using dedicated masking techniques.
Is there a tool to check how much credit I have left? +
Yes, use get_credit_balance. Running this first prevents you from hitting unexpected payment issues when running large batch jobs.
What should I use if I only need to change one small part of an image? +
Use the inpaint_image tool. It lets you draw a mask over a specific area and guide the replacement with a precise text prompt, keeping everything else intact.
Before starting a project, how do I use the `list_engines` tool to check compatibility? +
It lists all available generation engines on Stability AI. This step confirms which models you can access and helps select the right one for your specific output requirements (e.g., Turbo vs. Large). Always run this first during setup.
When using `text_to_image_v1`, what are the necessary constraints for width and height? +
The dimensions must be multiples of 64. The tool requires you to specify these exact values along with an engine ID and detailed prompt. You can't just guess; the numbers have to fit the model's requirements.
How does `image_to_image_v1` handle changes when I don't want major structural shifts? +
It transforms an existing image based on a prompt, but you can control the degree of change. The tool requires both an engine ID and a prompt to guide the transformation process. You specify how much it should adapt.
If I run multiple generation calls using `generate_core_v2`, is there any rate limiting I need to be aware of? +
Rate limits are tied to your API plan and credit balance. Always check the documentation for specific usage caps per minute or hour. Using get_credit_balance helps you monitor consumption before hitting a limit.
Can the language model visually analyze the specific outputs generated from the APIs recursively? +
Stability AI’s underlying architecture transmits the resultant graphical files returning strictly formatted Base64 encoded outputs or structured CDN links. Assuming your interface possesses multimodal capability, it is logically capable of assessing graphic properties accurately post generation.
Who possesses ownership over the raw generated assets computationally rendered here? +
By adhering definitively to official usage terms, creations executed natively belong securely entirely to the account producing them (you). Generative visual models execute entirely adhering to a clear framework of user copyright ownership fundamentally aligned with typical platform specifications.
Which image formats does the API return? +
The API typically returns PNG images encoded as Base64 strings or direct CDN URLs. The exact format depends on the endpoint and parameters used in the generation request.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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