MCP Server
The Pexelize MCP Server is currently in development and will be available soon. This page provides an early look at what's coming and how it will work.
Introduction
The Pexelize MCP Server allows you to connect your AI agents and clients with the Pexelize editor SDK. It makes key functionality of the Pexelize editor — the visual builder, templates, and export APIs — accessible to AI agents, opening up new ways to bring agentic design directly into your application.
The MCP server uses the open Model Context Protocol standard, which means it works with any LLM provider that supports MCP — including Claude, GPT, Gemini, and open-source models.
- The MCP server requires a live and correctly configured Pexelize editor session to function. The editor must be running with MCP enabled before using any tools — headless operation without the editor UI is not supported.
- The MCP server provides access to Pexelize editor tools for AI agents. However, providing the agent is the responsibility of your application.
What can you do with the Pexelize MCP Server?
The Pexelize MCP server empowers your product team to seamlessly and quickly integrate AI-driven design processes into your Pexelize-powered application. This lets your end users leverage AI in creating, editing, and optimizing their email designs — reducing friction between idea and execution.
By replacing expensive custom integrations with a universal standard for AI connectivity, the Pexelize MCP server delivers:
- Drastic reduction in time-to-market — Deploy AI-powered features in hours instead of weeks or months for your embedded email builder.
- A future-proof AI strategy — MCP decouples your application and data from the specific AI model you are using, so you can swap AI providers without rebuilding infrastructure.
- Magic end-user experience — Deep integration supercharges AI capabilities, with your users achieving professional results in seconds with zero learning curve.
Use Cases
Prompt to design
Create complete, high-quality email designs from scratch with a single natural-language prompt. The AI agent uses the MCP server's tools to lay out sections, add content blocks, apply styling, and produce a production-ready template.
Instant rebranding
Edit existing email designs by applying a different brand identity or color palette via AI. Pass a brand guide or color scheme to the agent and let it update every element in the template automatically.
Content iteration
Generate content variations — different headlines, body copy, CTAs — while maintaining your core brand elements. The AI agent can swap text, adjust tone, and create A/B test variants without touching the layout.
MCP Server & AI Agent Responsibilities
In the MCP architecture, responsibilities are split to ensure that AI models can access tools and data without needing custom code for every single integration.
The Pexelize MCP Server acts as the "Hands" — it exposes tools that create and edit emails in the Pexelize editor. The AI Agent acts as the "Brain" — it knows what the user wants and which tools to call to achieve that goal.
| MCP Server | AI Agent | |
|---|---|---|
| Primary role | The connector that exposes tools, resources, and prompts from the Pexelize editor | The host application (your AI agent) that manages the user session and the LLM |
| Initiation | Waits for requests. Responds with a list of available tools, resources, and prompts | Starts the connection. Discovers what the server is capable of via a handshake |
| Logic execution | Executes the work — performs the actual editor operation, export, or template manipulation | Orchestrates the workflow — decides when to call a tool based on the model's intent |
| Capabilities | Provides Tools (actions) and Resources (data) for the Pexelize editor | Provides Sampling (allows server to use the host's LLM) and Roots (defines scope boundaries) |
| Security | Defines scope — implements access logic and data filtering for editor operations | Enforces permissions — asks the user for consent before a tool runs or data is accessed |
| Model awareness | Model-agnostic — works with any LLM that supports MCP (Claude, GPT, Gemini, etc.) | Knows which LLM is being used and formats data for its context window |
Stay updated
The MCP Server is actively in development. To stay informed about the release:
- Follow us on GitHub
- Check the Pexelize website for updates
- Reach out to [email protected] with questions or feedback