How to Use the DBeaver (CloudBeaver) MCP in LlamaIndex

Q: Can LlamaIndex query the user list from DBeaver (CloudBeaver)?

Absolutely. You'd use the getadminuserinfo tool to fetch data for each user and then ingest it into a LlamaIndex vector store. After that, you can query your user base using natural language.

Q: How do I build a RAG app over my DBeaver (CloudBeaver) configurations using LlamaIndex?

First, use tools like getconnectioninfo to fetch all your connection configs. Then, use a LlamaIndex data loader to index that JSON output. Now your agent can answer questions about your DBeaver (CloudBeaver) setup.

Q: Can I use LlamaIndex to find out which auth providers are set up in DBeaver (CloudBeaver)?

Yes. Call the getauthproviders tool and index the results. This lets your LlamaIndex application answer questions about security configurations, like whether SAML is enabled on your DBeaver (CloudBeaver) instance.

Q: What's the difference between querying live vs. indexed data for DBeaver (CloudBeaver) in LlamaIndex?

Live data is pulled directly via an MCP tool call like getactiveuser. Indexed data is a snapshot you've already stored, letting you ask historical questions without hitting the live API.

Turn your DBeaver (CloudBeaver) server into a queryable knowledge base with LlamaIndex. Ask questions about users and configs.

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Connect DBeaver (CloudBeaver) MCP to LlamaIndex

Create your Vinkius account to connect DBeaver (CloudBeaver) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup DBeaver (CloudBeaver) with LlamaIndex

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Index Your Server's Configuration

Use the DBeaver tools to build a full index of your server's state. Run `get_admin_user_info` for all users, `get_connection_info` for all connections, and `get_all_product_licenses` for licensing details. LlamaIndex ingests this output. Now you have a searchable knowledge base. You can ask your RAG app natural language questions like, 'Which users have access to the production Postgres connection?' and get an answer grounded in the actual, indexed configuration data.

Query Live and Historical State

Your agent doesn't have to choose between fresh data and indexed history. It can use both. The agent can call `get_driver_list` or `get_auth_providers` to get the current server status. Then, it can compare that live data against the indexed data from last week to answer questions like, 'What new auth providers were added recently?'. LlamaIndex excels at this kind of time-based analysis.

Grounded Actions with This MCP Server

Actions become smarter when they're informed by indexed knowledge. Before your agent attempts to export data with `data_transfer_export_data_from_container`, it can query the LlamaIndex vector store to verify its permissions and find the correct connection ID. This reduces errors. The agent can also check which export formats are available with `data_transfer_available_stream_processors`. It's not just executing blindly; it's using the MCP tools to make informed decisions based on the knowledge it has.

Setup guide

Set up DBeaver (CloudBeaver) 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 DBeaver (CloudBeaver) 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 DBeaver (CloudBeaver) tools.",
)
response = await agent.run("List recent DBeaver (CloudBeaver) data")

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Common questions about DBeaver (CloudBeaver) MCP in LlamaIndex

Absolutely. You'd use the `get_admin_user_info` tool to fetch data for each user and then ingest it into a LlamaIndex vector store. After that, you can query your user base using natural language.

First, use tools like `get_connection_info` to fetch all your connection configs. Then, use a LlamaIndex data loader to index that JSON output. Now your agent can answer questions about your DBeaver (CloudBeaver) setup.

Yes. Call the `get_auth_providers` tool and index the results. This lets your LlamaIndex application answer questions about security configurations, like whether SAML is enabled on your DBeaver (CloudBeaver) instance.

Live data is pulled directly via an MCP tool call like `get_active_user`. Indexed data is a snapshot you've already stored, letting you ask historical questions without hitting the live API.

Yes, because you control what gets indexed. The server exposes metadata like server configurations and license keys, not the data in your databases. Each call to the MCP Server is executed in a V8 Isolate sandbox on Vinkius, completely separate from other tenants.

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