Xata (Serverless DB) MCP Server for LlamaIndexGive LlamaIndex instant access to 30 tools to Create Branch, Create Organization, Create Project, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Xata (Serverless DB) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The Xata (Serverless DB) MCP Server for LlamaIndex is a standout in the Databases category — giving your AI agent 30 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Xata (Serverless DB). "
"You have 30 tools available."
),
)
response = await agent.run(
"What tools are available in Xata (Serverless DB)?"
)
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 Xata (Serverless DB) MCP Server
Connect your Xata account to any AI agent to manage your serverless data infrastructure through natural conversation. Xata combines the power of a relational database with the ease of use of a spreadsheet, all accessible via this MCP server.
LlamaIndex agents combine Xata (Serverless DB) tool responses with indexed documents for comprehensive, grounded answers. Connect 30 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
- Organization Management — List, create, and update organizations and manage team members or invitations using
list_organizationsandlist_members. - Project & Branch Control — Create and manage projects and database branches across different regions with
create_projectandcreate_branch. - SQL Execution — Run SQL queries or batches directly against your database branches to fetch or manipulate data using
execute_sql. - Infrastructure Insights — Retrieve branch metrics, credentials, and project limits to monitor your serverless environment with
get_branch_metricsandget_project_limits.
The Xata (Serverless DB) MCP Server exposes 30 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 30 Xata (Serverless DB) tools available for LlamaIndex
When LlamaIndex connects to Xata (Serverless DB) through Vinkius, your AI agent gets direct access to every tool listed below — spanning serverless, sql, database-migration, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Create branch on Xata (Serverless DB)
Create a new database branch
Create organization on Xata (Serverless DB)
Create a new organization
Create project on Xata (Serverless DB)
Create a new project
Delete branch on Xata (Serverless DB)
Delete a branch
Delete invitation on Xata (Serverless DB)
Delete an organization invitation
Delete project on Xata (Serverless DB)
Delete a project
Execute sql on Xata (Serverless DB)
Execute a SQL query against a branch
Execute sql batch on Xata (Serverless DB)
Execute a batch of SQL queries in a single transaction
Get branch on Xata (Serverless DB)
Get branch details
Get branch credentials on Xata (Serverless DB)
Retrieve branch credentials
Get branch metrics on Xata (Serverless DB)
Retrieve branch metrics
Get organization on Xata (Serverless DB)
Get organization details
Get project on Xata (Serverless DB)
Get project details
Get project limits on Xata (Serverless DB)
Get resource limits for projects
List branches on Xata (Serverless DB)
List all branches in a project
List images on Xata (Serverless DB)
List available images
List instance types on Xata (Serverless DB)
List available instance types for a region
List invitations on Xata (Serverless DB)
List organization invitations
List members on Xata (Serverless DB)
List organization members
List organizations on Xata (Serverless DB)
List all organizations
List projects on Xata (Serverless DB)
List all projects in an organization
List regions on Xata (Serverless DB)
List available regions
Remove member on Xata (Serverless DB)
Remove a member from an organization
Request organization deletion on Xata (Serverless DB)
Request organization deletion
Resend invitation on Xata (Serverless DB)
Resend an organization invitation
Rotate branch credentials on Xata (Serverless DB)
Rotate branch credentials
Send invitation on Xata (Serverless DB)
Send an invitation to join an organization
Update branch on Xata (Serverless DB)
Update branch details
Update organization on Xata (Serverless DB)
Update organization details
Update project on Xata (Serverless DB)
Update project details
Connect Xata (Serverless DB) to LlamaIndex via MCP
Follow these steps to wire Xata (Serverless DB) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Xata (Serverless DB) MCP Server
LlamaIndex provides unique advantages when paired with Xata (Serverless DB) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Xata (Serverless DB) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Xata (Serverless DB) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Xata (Serverless DB), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Xata (Serverless DB) tools were called, what data was returned, and how it influenced the final answer
Xata (Serverless DB) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Xata (Serverless DB) MCP Server delivers measurable value.
Hybrid search: combine Xata (Serverless DB) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Xata (Serverless DB) 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 Xata (Serverless DB) for fresh data
Analytical workflows: chain Xata (Serverless DB) queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Xata (Serverless DB) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Xata (Serverless DB) immediately.
"List all my Xata organizations and their details."
"Show me all branches for project 'my-database' in organization 'org-123'."
"Execute SQL: SELECT name, email FROM users WHERE active = true LIMIT 5 on the main branch."
Troubleshooting Xata (Serverless DB) MCP Server with LlamaIndex
Common issues when connecting Xata (Serverless DB) to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpXata (Serverless DB) + LlamaIndex FAQ
Common questions about integrating Xata (Serverless DB) 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?
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