Iterable MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Iterable as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Iterable. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Iterable?"
)
print(response)
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.
LlamaIndex agents combine Iterable tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
The Iterable MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Iterable MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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
Why Use LlamaIndex with the Iterable MCP Server
LlamaIndex provides unique advantages when paired with Iterable through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Iterable tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Iterable tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Iterable, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Iterable tools were called, what data was returned, and how it influenced the final answer
Iterable + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Iterable MCP Server delivers measurable value.
Hybrid search: combine Iterable real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Iterable to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Iterable for fresh data
Analytical workflows: chain Iterable queries with LlamaIndex's data connectors to build multi-source analytical reports
Iterable MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Iterable to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Iterable to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpIterable + LlamaIndex FAQ
Common questions about integrating Iterable MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.