OceanBase 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 OceanBase 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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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 OceanBase. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in OceanBase?"
)
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 OceanBase MCP Server
Empower your AI agent to orchestrate your entire database infrastructure with OceanBase, the premier enterprise distributed relational database. By connecting OceanBase to your agent, you transform complex cluster management, tenant resource allocation, and database auditing into a natural conversation. Your agent can instantly list your database clusters, retrieve detailed configuration for tenants, monitor resource usage statistics, and browse available databases without you ever needing to navigate the OceanBase Cloud console. Whether you are conducting a capacity planning review or auditing database health across a global deployment, your agent acts as a real-time database reliability assistant, keeping your data infrastructure accurate and your systems performant.
LlamaIndex agents combine OceanBase 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
- Cluster Orchestration — List all database clusters and retrieve detailed configuration and status information.
- Tenant Management — Browse logical tenants within clusters and retrieve detailed resource allocation metadata.
- Database Auditing — List all databases within specific tenants to identify data assets and structures.
- Resource Monitoring — Retrieve aggregate resource usage statistics to audit system performance and capacity.
- Organization Insights — Browse projects, instances, and workspaces to maintain a unified view of your database ecosystem.
The OceanBase 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 OceanBase to LlamaIndex via MCP
Follow these steps to integrate the OceanBase 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 OceanBase
Why Use LlamaIndex with the OceanBase MCP Server
LlamaIndex provides unique advantages when paired with OceanBase through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OceanBase tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OceanBase tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OceanBase, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OceanBase tools were called, what data was returned, and how it influenced the final answer
OceanBase + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OceanBase MCP Server delivers measurable value.
Hybrid search: combine OceanBase real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OceanBase 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 OceanBase for fresh data
Analytical workflows: chain OceanBase queries with LlamaIndex's data connectors to build multi-source analytical reports
OceanBase MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect OceanBase to LlamaIndex via MCP:
get_cluster_details
Get cluster details
get_instance_details
Get instance details
get_resource_stats
Get resource statistics
get_tenant_details
Get tenant details
get_workspaces
Get account workspaces
list_clusters
List OceanBase clusters
list_databases
List tenant databases
list_instances
List OB instances
list_projects
List OB projects
list_tenants
List cluster tenants
Example Prompts for OceanBase in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OceanBase immediately.
"List all database clusters in my OceanBase account."
"Show me the resource usage statistics for the organization."
"List all databases in tenant 'tenant-8821' inside cluster 'cluster-9920'."
Troubleshooting OceanBase MCP Server with LlamaIndex
Common issues when connecting OceanBase to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOceanBase + LlamaIndex FAQ
Common questions about integrating OceanBase 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 OceanBase 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 OceanBase to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.