How to Use the Xata (Serverless DB) MCP in LlamaIndex

Q: What specific metrics can LlamaIndex index from Xata (Serverless DB)?

You can query branch metrics using getbranchmetrics. These results include resource usage, read/write counts, and latency data. LlamaIndex stores these numerical values for accurate retrieval.

Q: How does LlamaIndex track project changes in Xata (Serverless DB)?

When a user runs updateproject or calls getproject, the resulting configuration details are captured. This allows your RAG application to answer questions like, 'What was the budget limit for Project Beta last quarter?'

Q: Can LlamaIndex use Xata (Serverless DB) data in a workflow?

Yes. By indexing outputs from listbranches or listorganizations, you can build applications that reference the current state of your serverless database structure without needing to run a live API call.

Q: How does LlamaIndex handle user access data in Xata (Serverless DB)?

The agent captures membership details using listmembers. This allows you to build a knowledge base that answers questions like, 'Who was added as an owner to the finance branch?'

Q: Does LlamaIndex index sensitive data types from Xata (Serverless DB)?

The server touches organization invitations. When you run listinvitations, the resulting invitation IDs and status are indexed, giving your knowledge base context about access control.

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Connect Xata (Serverless DB) MCP to LlamaIndex

Create your Vinkius account to connect Xata (Serverless DB) 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 Xata (Serverless DB) with LlamaIndex

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Indexing DB Performance Data

LlamaIndex can turn database metrics into searchable knowledge. The agent retrieves operational performance via `get_branch_metrics`, which then becomes part of your indexed corpus. You query past sessions and get answers grounded in the exact usage data provided by Xata. This means you don't have to guess about bottlenecks; you just ask, 'What were our peak read counts last month?' and the index points directly to that historical metric.

Tracking Organization Structure Changes

The framework indexes structural changes. If a user calls `update_organization` or `list_projects`, LlamaIndex captures those details. You can then query your knowledge base years later and ask, 'What was the original name of Project Alpha?' It builds a comprehensive record of how the entire database environment evolved over time.

Batch Processing for Knowledge Bases

Sometimes you need to process massive amounts of data before indexing. LlamaIndex allows the agent to use `execute_sql_batch` to run multiple queries in one transaction. This output is then captured and made available as a single, large knowledge chunk. Instead of running 50 individual calls, the client executes one batch operation, making the resulting data highly efficient for vector embedding.

Setup guide

Set up Xata (Serverless DB) 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 Xata (Serverless DB) 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 Xata (Serverless DB) tools.",
)
response = await agent.run("List recent Xata (Serverless DB) data")

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Common questions about Xata (Serverless DB) MCP in LlamaIndex

You can query branch metrics using `get_branch_metrics`. These results include resource usage, read/write counts, and latency data. LlamaIndex stores these numerical values for accurate retrieval.

When a user runs `update_project` or calls `get_project`, the resulting configuration details are captured. This allows your RAG application to answer questions like, 'What was the budget limit for Project Beta last quarter?'

Yes. By indexing outputs from `list_branches` or `list_organizations`, you can build applications that reference the current state of your serverless database structure without needing to run a live API call.

The agent captures membership details using `list_members`. This allows you to build a knowledge base that answers questions like, 'Who was added as an owner to the finance branch?'

The server touches organization invitations. When you run `list_invitations`, the resulting invitation IDs and status are indexed, giving your knowledge base context about access control.

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