Iterable MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Iterable through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"iterable": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Iterable, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Iterable through native MCP adapters. Connect 10 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
The Iterable MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 LangChain via MCP
Follow these steps to integrate the Iterable MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from Iterable via MCP
Why Use LangChain with the Iterable MCP Server
LangChain provides unique advantages when paired with Iterable through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Iterable MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Iterable queries for multi-turn workflows
Iterable + LangChain Use Cases
Practical scenarios where LangChain combined with the Iterable MCP Server delivers measurable value.
RAG with live data: combine Iterable tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Iterable, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Iterable tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Iterable tool call, measure latency, and optimize your agent's performance
Iterable MCP Tools for LangChain (10)
These 10 tools become available when you connect Iterable to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Iterable to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersIterable + LangChain FAQ
Common questions about integrating Iterable MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.