How to Use the OceanBase MCP in LlamaIndex

Q: How do I index OceanBase metadata using LlamaIndex?

Use llama-index-tools-mcp to load the OceanBase tools into your environment. The client executes listdatabases and indexes the returned schema metadata directly into your vector store for semantic retrieval.

Q: Can LlamaIndex query live OceanBase performance metrics?

Yes, by calling getresourcestats through the MCP tool spec. LlamaIndex retrieves real-time CPU and memory metrics, incorporating them directly into the LLM's context window for live analysis.

Q: How does the OceanBase MCP Server prevent hallucinations in LlamaIndex?

It forces the agent to rely on concrete tools like getinstancedetails and gettenantdetails for infrastructure data. LlamaIndex bases its answers on these verified tool outputs rather than pre-trained assumptions.

Q: Can I filter which OceanBase tools are exposed to my LlamaIndex agent?

Yes, you can use the allowedtools filter in the LlamaIndex MCP tool specification. This lets you restrict the agent to read-only operations like listclusters while hiding administrative tools.

Q: What data privacy controls protect my OceanBase schema data in LlamaIndex?

Only database metadata, such as tenant names from listtenants and workspace configurations from getworkspaces, is processed. The Vinkius V8 sandbox secures this metadata, ensuring no actual table rows are exposed to LlamaIndex.

Index OceanBase cluster metadata into your LlamaIndex vector store for semantic search and hallucination-free retrieval.

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Setup OceanBase with LlamaIndex

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Semantic search over OceanBase schemas with LlamaIndex

`list_databases` retrieves your active database schemas and indexes them directly into a LlamaIndex vector store. Your LlamaIndex agent queries this index to find which database holds specific tables, bypassing manual schema lookups. This indexing prevents your agent from guessing database structures. You get a searchable knowledge base grounded in the actual, live schema layout of your OceanBase deployment.

Context-aware cluster troubleshooting in LlamaIndex

`get_resource_stats` pulls live memory and CPU metrics from your OceanBase instances to augment your LlamaIndex RAG pipeline. When an alert fires, the agent queries this performance data to diagnose resource constraints. By combining historical logs with live statistics, LlamaIndex provides context-rich answers about your cluster health. You stop guessing why a node is slow and get direct, data-backed diagnostics.

Grounding LlamaIndex agents with OceanBase MCP Server tools

`list_instances` exposes your physical deployment topology directly to the LlamaIndex query engine. The engine uses this structural map to ground its answers, ensuring it never references non-existent instances. This tight coupling between the MCP Server and LlamaIndex eliminates database configuration hallucinations. Your agent knows the exact state of your OceanBase infrastructure before it answers a single user query.

Setup guide

Set up OceanBase MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all OceanBase MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to OceanBase tools.",
)
response = await agent.run("List recent OceanBase data")

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Common questions about OceanBase MCP in LlamaIndex

Use `llama-index-tools-mcp` to load the OceanBase tools into your environment. The client executes `list_databases` and indexes the returned schema metadata directly into your vector store for semantic retrieval.

Yes, by calling `get_resource_stats` through the MCP tool spec. LlamaIndex retrieves real-time CPU and memory metrics, incorporating them directly into the LLM's context window for live analysis.

It forces the agent to rely on concrete tools like `get_instance_details` and `get_tenant_details` for infrastructure data. LlamaIndex bases its answers on these verified tool outputs rather than pre-trained assumptions.

Yes, you can use the `allowed_tools` filter in the LlamaIndex MCP tool specification. This lets you restrict the agent to read-only operations like `list_clusters` while hiding administrative tools.

Only database metadata, such as tenant names from `list_tenants` and workspace configurations from `get_workspaces`, is processed. The Vinkius V8 sandbox secures this metadata, ensuring no actual table rows are exposed to LlamaIndex.

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