RudderStack MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add RudderStack 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 RudderStack. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in RudderStack?"
)
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 RudderStack MCP Server
Connect your conversational assistant directly into RudderStack, the leading enterprise Customer Data Platform (CDP) dedicated to developers. This integration robustly morphs your AI into a dynamic data engineer, enabling smooth real-time conversational audits encompassing configured sources, end tracking pipeline connections, tracking plans, and segmentation.
LlamaIndex agents combine RudderStack tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Explore Inlets and Outlets — Command the assistant directly natively to list every active data intake platform (
list_sources) securely or drill flawlessly deep into individual setup environments using detailed metrics (get_source). View every downstream endpoint gracefully (list_destinations). - Audit Data Interconnectivity — Are the web analytics pipelines correctly tied proactively to the respective data warehouses? The AI natively verifies data pipeline flows mapping seamlessly directly (
list_connections). - Governance & Audience Mapping — Instantly review strict operational event typing mappings natively securely configured (
list_tracking_plans), or query active personalized remarketing sub-clusters synced locally to customer databases cleanly natively (list_audiences).
The RudderStack MCP Server exposes 7 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 RudderStack to LlamaIndex via MCP
Follow these steps to integrate the RudderStack 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 7 tools from RudderStack
Why Use LlamaIndex with the RudderStack MCP Server
LlamaIndex provides unique advantages when paired with RudderStack through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine RudderStack tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain RudderStack tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query RudderStack, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what RudderStack tools were called, what data was returned, and how it influenced the final answer
RudderStack + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the RudderStack MCP Server delivers measurable value.
Hybrid search: combine RudderStack real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query RudderStack 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 RudderStack for fresh data
Analytical workflows: chain RudderStack queries with LlamaIndex's data connectors to build multi-source analytical reports
RudderStack MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect RudderStack to LlamaIndex via MCP:
get_destination
Retrieves details for a specific data destination
get_source
Retrieves details for a specific data source
list_audiences
Lists all defined user audiences
list_connections
Lists all source-to-destination connections
list_destinations
Lists all data destinations configured in RudderStack
list_sources
Lists all data sources configured in RudderStack
list_tracking_plans
Lists all tracking plans defined in the data catalog
Example Prompts for RudderStack in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with RudderStack immediately.
"List all configured sources."
"Check if the connection between our website source and Snowflake destination is active."
"Show me the tracking plans currently applied to our iOS app source."
Troubleshooting RudderStack MCP Server with LlamaIndex
Common issues when connecting RudderStack to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpRudderStack + LlamaIndex FAQ
Common questions about integrating RudderStack 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 RudderStack 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 RudderStack to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.