GrowingIO 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 GrowingIO as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 GrowingIO. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in GrowingIO?"
)
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 GrowingIO MCP Server
Empower your AI agent to orchestrate your product analytics and user behavioral data with GrowingIO, the premier analytical platform in China. By connecting GrowingIO to your agent, you transform complex event tracking, user segmentation, and metric analysis into a natural conversation. Your agent can instantly list tracked events, retrieve detailed user segment metadata, monitor conversion funnels, and execute quantitative metric queries without you ever needing to navigate the comprehensive GrowingIO web interface. Whether you are conducting a product health audit or monitoring real-time campaign performance, your agent acts as a real-time data analyst assistant, keeping your product data accurate and your growth moving.
LlamaIndex agents combine GrowingIO 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.
What you can do
- Event Orchestration — List and retrieve detailed metadata for all tracked behavioral events in your project.
- User Segmentation — Browse and monitor user segments to identify high-value cohorts and behavioral patterns.
- Metric Querying — Execute quantitative queries to retrieve specific performance metrics via natural language.
- Funnel Auditing — Retrieve detailed configuration and data for conversion funnels to identify drop-off points.
- Campaign Insights — Browse tracked advertising campaigns and identify successful growth drivers.
The GrowingIO 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 GrowingIO to LlamaIndex via MCP
Follow these steps to integrate the GrowingIO 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 GrowingIO
Why Use LlamaIndex with the GrowingIO MCP Server
LlamaIndex provides unique advantages when paired with GrowingIO through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GrowingIO tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GrowingIO tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GrowingIO, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GrowingIO tools were called, what data was returned, and how it influenced the final answer
GrowingIO + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GrowingIO MCP Server delivers measurable value.
Hybrid search: combine GrowingIO real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GrowingIO 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 GrowingIO for fresh data
Analytical workflows: chain GrowingIO queries with LlamaIndex's data connectors to build multi-source analytical reports
GrowingIO MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect GrowingIO to LlamaIndex via MCP:
get_event
Get event details
get_funnel
Get conversion funnel details
get_metrics
Query project metrics
get_project_info
Get project metadata
get_segment_users
Get users in a segment
list_ads
List advertising campaigns
list_events
List project events
list_log_sources
). List data log sources
list_segments
List user segments
list_variables
List tracked variables
Example Prompts for GrowingIO in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GrowingIO immediately.
"List all user segments in GrowingIO."
"Show me the conversion funnel for 'Checkout Flow'."
"Query the DAU for the last 7 days."
Troubleshooting GrowingIO MCP Server with LlamaIndex
Common issues when connecting GrowingIO to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGrowingIO + LlamaIndex FAQ
Common questions about integrating GrowingIO 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 GrowingIO 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 GrowingIO to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.