StarRocks 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 StarRocks 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 StarRocks. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in StarRocks?"
)
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 StarRocks MCP Server
Empower your AI agent to orchestrate your high-performance OLAP infrastructure with StarRocks, the leading distributed analytical database. By connecting StarRocks to your agent, you transform complex cluster auditing, schema management, and data querying into a natural conversation. Your agent can instantly list databases, retrieve table schemas, monitor backend nodes, and even execute complex SQL queries without you ever needing to open a SQL terminal or the StarRocks Manager. Whether you are conducting a data audit or monitoring real-time ingestion jobs, your agent acts as a real-time data reliability assistant, keeping your analytical platform accurate and your insights moving.
LlamaIndex agents combine StarRocks 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
- Database Orchestration — List all databases and retrieve detailed table schemas and structures.
- Analytical Querying — Execute arbitrary SQL queries directly through the agent to retrieve real-time insights.
- Cluster Monitoring — Browse status and metadata for Frontend (FE) and Backend (BE) nodes to audit health.
- Ingestion Control — Monitor data load jobs and historical ingestion performance for your analytical pipelines.
- Storage Insights — Retrieve disk usage and data size statistics across the entire distributed cluster.
The StarRocks 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 StarRocks to LlamaIndex via MCP
Follow these steps to integrate the StarRocks 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 StarRocks
Why Use LlamaIndex with the StarRocks MCP Server
LlamaIndex provides unique advantages when paired with StarRocks through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine StarRocks tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain StarRocks tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query StarRocks, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what StarRocks tools were called, what data was returned, and how it influenced the final answer
StarRocks + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the StarRocks MCP Server delivers measurable value.
Hybrid search: combine StarRocks real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query StarRocks 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 StarRocks for fresh data
Analytical workflows: chain StarRocks queries with LlamaIndex's data connectors to build multi-source analytical reports
StarRocks MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect StarRocks to LlamaIndex via MCP:
execute_query
Execute arbitrary SQL query
get_cluster_info
Get frontend nodes info
get_storage_usage
Get data storage statistics
get_table_schema
Get table structure
list_databases
List all databases
list_jobs
List data load jobs
list_mvs
List materialized views
list_nodes
List backend nodes
list_tables
List tables in a database
list_views
List database views
Example Prompts for StarRocks in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with StarRocks immediately.
"List all databases in my StarRocks cluster."
"Show me the average order value from the 'sales' table."
"Check for any offline backend nodes."
Troubleshooting StarRocks MCP Server with LlamaIndex
Common issues when connecting StarRocks to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpStarRocks + LlamaIndex FAQ
Common questions about integrating StarRocks 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 StarRocks 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 StarRocks to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.