How to Use the Xata MCP in LlamaIndex

Q: How do I get a list of all member accounts using Xata and LlamaIndex?

You call listorganizationmembers to retrieve the full roster. This data, which includes user emails and roles, is indexed by LlamaIndex. You can then query your knowledge base for specific users or groups.

Q: What information does Xata provide about project deployment regions?

You use listregions to get a list of available geographic areas where the project can deploy. This data is clean and structured, making it perfect for LlamaIndex to ingest into your knowledge index.

Q: If I need historical access control info using Xata and LlamaIndex?

You check pending invitations by running listorganizationinvitations. The resulting status (pending, expired) is indexed. This provides a searchable record of your organization's membership lifecycle.

Q: How can I track changes to GitHub repository mappings using Xata and LlamaIndex?

You call getgithubrepositorymapping. The current mapping details are then indexed, allowing you to quickly retrieve the source code connection status for any given branch from your knowledge base.

Build a queryable knowledge index from Xata serverless database data using LlamaIndex.

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Connect Xata MCP to LlamaIndex

Create your Vinkius account to connect Xata 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 with LlamaIndex

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Index organizational structure metadata with the MCP Server.

LlamaIndex can treat structural information as searchable context. You run `list_organizations` and index every returned organization name, ID, and description into your vector store. This allows you to query past sessions or configurations and get answers grounded in actual API data. Similarly, calling `get_project_limits` provides resource metrics (like CPU quotas) that become part of your knowledge base. You can now ask, 'What were the project limits for billing last month?'

Retrieve and index credentials using Xata and LlamaIndex.

You don't have to hardcode secrets; you let LlamaIndex read them. Running `get_branch_credentials` pulls the username and password for a branch, which is then indexed as factual knowledge. This helps build RAG applications where sensitive context is retrieved and used without being misused. Furthermore, listing keys via `list_user_api_keys` adds credential metadata to your index, making it searchable alongside documents.

Run queries and index the results using the MCP Server.

The core mechanism here is indexing output. You execute a query using `execute_sql`. Instead of just displaying the data, LlamaIndex captures the resulting rows and indexes them. This means your knowledge base can answer questions like, 'What were the top 5 records returned when I ran this specific SQL batch?' This combination lets you combine live API data with documents into one unified index.

Setup guide

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

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Common questions about Xata MCP in LlamaIndex

You call `list_organization_members` to retrieve the full roster. This data, which includes user emails and roles, is indexed by LlamaIndex. You can then query your knowledge base for specific users or groups.

You use `list_regions` to get a list of available geographic areas where the project can deploy. This data is clean and structured, making it perfect for LlamaIndex to ingest into your knowledge index.

You check pending invitations by running `list_organization_invitations`. The resulting status (pending, expired) is indexed. This provides a searchable record of your organization's membership lifecycle.

You call `get_github_repository_mapping`. The current mapping details are then indexed, allowing you to quickly retrieve the source code connection status for any given branch from your knowledge base.

This server manages and exposes organizational membership lists (`list_organization_members`) and user API keys. The data type is structured access control information.

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