Bring Email Templates
to Pydantic AI
Learn how to connect MJML (Email Markup) to Pydantic AI and start using 1 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the MJML (Email Markup) MCP Server?
Connect the MJML engine to your AI agent to generate professional, responsive email templates using natural language. MJML is the industry standard for ensuring emails look great across all clients like Outlook, Gmail, and Apple Mail.
What you can do
- Responsive Rendering — Convert MJML XML or JSON strings into production-ready HTML in seconds
- Email Prototyping — Rapidly iterate on email designs within your chat or code editor
- Best Practices — Ensure your markup follows email client standards automatically without manual table hacking
How it works
- Subscribe to this server
- Enter your MJML Application ID and API Key
- Start rendering email markup from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Developers — Generate email HTML without leaving the IDE or dealing with complex CSS inlining
- Marketing Teams — Quickly preview how MJML-based campaigns will look before deployment
- Designers — Validate MJML syntax and see immediate visual results through the AI presenter
Built-in capabilities (1)
Provide the raw MJML XML or JSON string. Render MJML markup to responsive HTML
Why Pydantic AI?
Pydantic AI validates every MJML (Email Markup) tool response against typed schemas, catching data inconsistencies at build time. Connect 1 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
- —
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your MJML (Email Markup) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your MJML (Email Markup) connection logic from agent behavior for testable, maintainable code
MJML (Email Markup) in Pydantic AI
MJML (Email Markup) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect MJML (Email Markup) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for MJML (Email Markup) in Pydantic AI
The MJML (Email Markup) MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 1 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
How Vinkius secures
MJML (Email Markup) for Pydantic AI
Every tool call from Pydantic AI to the MJML (Email Markup) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I render MJML from a JSON string instead of XML?
Yes! The render_mjml tool accepts both raw MJML XML and MJML JSON strings. The engine will automatically detect the format and transpile it into responsive HTML.
What is the maximum size of the generated HTML output?
The integration supports an egress limit of up to 5MB, which is more than enough for even the most complex and content-heavy responsive email templates.
Does the tool validate my MJML syntax during rendering?
Yes, the render_mjml tool uses the official MJML engine which performs validation during the transpilation process to ensure the resulting HTML is compliant with email client best practices.
How does Pydantic AI discover MCP tools?
Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.
Does Pydantic AI validate MCP tool responses?
Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.
Can I switch LLM providers without changing MCP code?
Absolutely. Pydantic AI abstracts the model layer. your MJML (Email Markup) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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