Iterable MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Iterable through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Iterable "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Iterable?"
)
print(result.data)
asyncio.run(main())
* 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
About Iterable MCP Server
Empower your AI agents to manage your cross-channel marketing with Iterable. This MCP server allows you to list campaigns, retrieve user profiles, track engagement metrics, manage contact lists, and view message templates directly through the Iterable API. Ideal for automating growth marketing and customer lifecycle management.
Pydantic AI validates every Iterable tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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.
The Iterable MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Iterable to Pydantic AI via MCP
Follow these steps to integrate the Iterable MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Iterable with type-safe schemas
Why Use Pydantic AI with the Iterable MCP Server
Pydantic AI provides unique advantages when paired with Iterable through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Iterable integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Iterable connection logic from agent behavior for testable, maintainable code
Iterable + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Iterable MCP Server delivers measurable value.
Type-safe data pipelines: query Iterable with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Iterable tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Iterable and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Iterable responses and write comprehensive agent tests
Iterable MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Iterable to Pydantic AI via MCP:
get_campaign
Returns message content, audience targeting, and scheduling settings. Use this to analyze the setup of a specific campaign. Retrieves details for a specific campaign
get_campaign_metrics
Essential for reporting on marketing ROI and audience engagement. Retrieves performance metrics for a specific campaign
get_user
Essential for deep intelligence on an individual subscriber. Retrieves details for a user by email
list_campaigns
Returns campaign names, IDs, and statuses. Use this to identify active outreach efforts or locate a specific campaign ID. Lists all marketing campaigns
list_channels
g., Marketing, Transactional). Essential for understanding the available paths for reaching users. Lists all communication channels
list_lists
Useful for identifying segments and groups of users for targeted messaging. Lists all contact lists
list_message_types
g., "Weekly Newsletter", "Welcome Email") defined in the account. Useful for auditing message categorization. Lists all message types
list_templates
) available in the account. Useful for identifying content assets used in campaigns. Lists all message templates
list_webhooks
Useful for auditing system integrations and data exports. Lists all configured webhooks
list_workflows
Useful for monitoring automated marketing logic and identifying trigger-based campaigns. Lists all automation workflows
Example Prompts for Iterable in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Iterable immediately.
"List all active marketing campaigns in my Iterable account."
"Show me the details for user 'customer@example.com'."
"Check the metrics for campaign ID '123'."
Troubleshooting Iterable MCP Server with Pydantic AI
Common issues when connecting Iterable to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiIterable + Pydantic AI FAQ
Common questions about integrating Iterable MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Iterable with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Iterable to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.