Customer.io 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 Customer.io as an MCP tool provider through the 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 Customer.io. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Customer.io?"
)
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 Customer.io MCP Server
Integrate Customer.io, the platform for sending personalized messages based on customer behavior, directly into your AI workflow. Manage your customer profiles, monitor automated campaigns, and track engagement metrics using natural language.
LlamaIndex agents combine Customer.io tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Customer Identification — Create or update customer profiles with behavioral attributes via the Identify API.
- Campaign Monitoring — List automated campaigns and retrieve real-time performance and engagement metrics.
- Broadcast & Newsletter Tracking — Track one-to-many broadcast messages and newsletter statuses.
- Segment Oversight — Explore dynamic and manual customer segments to understand your audience composition.
The Customer.io 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 Customer.io to LlamaIndex via MCP
Follow these steps to integrate the Customer.io 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 Customer.io
Why Use LlamaIndex with the Customer.io MCP Server
LlamaIndex provides unique advantages when paired with Customer.io through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Customer.io tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Customer.io tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Customer.io, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Customer.io tools were called, what data was returned, and how it influenced the final answer
Customer.io + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Customer.io MCP Server delivers measurable value.
Hybrid search: combine Customer.io real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Customer.io 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 Customer.io for fresh data
Analytical workflows: chain Customer.io queries with LlamaIndex's data connectors to build multi-source analytical reports
Customer.io MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Customer.io to LlamaIndex via MCP:
get_campaign_performance
Resolves sent, opened, clicked, and converted counts. Interacts with the analytics and reporting engine. Get delivery and engagement metrics for a campaign
get_customer_details
Resolves custom attributes, device tokens, and segment memberships. Touches the granular profile and behavioral data boundary. Get full profile, attributes, and devices for a specific customer
get_engagement_summary
Resolves high-level engagement KPIs. Interacts with the global analytics boundary. Retrieve a high-level summary of campaign and broadcast performance
identify_customer
Resolves the identification status and profile state. Mutates the workspace identity database. Create or update a customer profile with attributes
list_automated_campaigns
Resolves campaign IDs, names, and trigger types. Interacts with the automation and messaging boundary. List all automated messaging campaigns
list_broadcast_messages
Resolves broadcast identifiers and scheduling metadata. Interacts with the bulk messaging boundary. List all one-to-many broadcast messages
list_customer_segments
Resolves segment IDs, types (manual/dynamic), and membership counts. Touches the audience segmentation and filtering boundary. List all dynamic and manual segments
list_customers
Resolves unique identifiers, email addresses, and last-seen timestamps. Interacts with the core identity and profile boundary. List all customers/people in your Customer.io workspace
list_newsletters
Resolves newsletter IDs and status. Touches the content distribution and newsletter management boundary. List all newsletter campaigns
search_customers_by_email
Resolves the associated customer identifiers. Touches the identity lookup and search boundary. Search for a customer profile by email address
Example Prompts for Customer.io in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Customer.io immediately.
"List all active automated campaigns in my workspace."
"Show me the performance metrics for the 'Welcome Sequence' campaign."
"Identify a new customer with ID 'user_789' and email 'new.user@example.com'."
Troubleshooting Customer.io MCP Server with LlamaIndex
Common issues when connecting Customer.io to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCustomer.io + LlamaIndex FAQ
Common questions about integrating Customer.io 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 Customer.io 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 Customer.io to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.