PlanetScale MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add PlanetScale 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
Vinkius supports streamable HTTP and SSE.
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 PlanetScale. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PlanetScale?"
)
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 PlanetScale MCP Server
Empower your AI agents to manage your PlanetScale serverless infrastructure seamlessly. Leverage the power of Vitess-backed MySQL without leaving your IDE. Ask your AI to branch a production database for testing, list regions, or drop obsolete schema forks instantly.
LlamaIndex agents combine PlanetScale tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Database Provisioning — Instantly list (
list_databases), inspect, create (create_database), or destroy serverless MySQL clusters running across global regions. - Branch Management — Harness PlanetScale's Git-like schema workflows. Direct your LLM to spawn a temporary
shadow-testbranch cloned frommain(create_branch), allowing consequence-free migrations before orchestrating Deploy Requests. - Infrastructure Exploration — Discover strict organizational IDs (
list_organizations) and query available physical cloud provider edges (list_regions) to optimize latency targets.
The PlanetScale MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect PlanetScale to LlamaIndex via MCP
Follow these steps to integrate the PlanetScale MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from PlanetScale
Why Use LlamaIndex with the PlanetScale MCP Server
LlamaIndex provides unique advantages when paired with PlanetScale through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PlanetScale tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PlanetScale tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PlanetScale, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PlanetScale tools were called, what data was returned, and how it influenced the final answer
PlanetScale + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PlanetScale MCP Server delivers measurable value.
Hybrid search: combine PlanetScale real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PlanetScale 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 PlanetScale for fresh data
Analytical workflows: chain PlanetScale queries with LlamaIndex's data connectors to build multi-source analytical reports
PlanetScale MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PlanetScale to LlamaIndex via MCP:
create_branch
Does *not* duplicate data (creates an empty schema clone of the parent) for secure CI testing uncoupled entirely from `main` load balancing layers. Fork a PlanetScale schema mapping to a new isolated Branch
create_database
Creates empty environments ready to execute explicit DDL definitions via non-blocking Deploy Requests. Provision a radically scalable Serverless Database instance
delete_branch
Utilized constantly within CI/CD pipelines following a successful Deploy Request morphing `main` schema structure directly. Purge an obsolete Git-like Schema testing ground
delete_database
Dropping the database effectively wipes terabytes of records scattered globally. Fails fully if unacknowledged connection logic binds it. Destroy a PlanetScale MySQL construct irreversibly
get_branch
Returns access hostnames for code integration. Deconstruct the layout of a single explicit Database Branch
get_database
Analyze core configuration of a specific MySQL cluster logic
list_branches
Essential for migrating schemas without locking production reads/writes. List Development Database Branches mirroring Prod architectures
list_databases
Retrieves explicitly mapping IDs orchestrating distributed Vitess backend shards. List high-availability PlanetScale MySQL DB distributions
list_organizations
Used solely to resolve the foundational string key prerequisite for all subsequent MySQL endpoint management. List root PlanetScale organizational identifiers
list_regions
Critical reference required during new Database/Branch physical provisioning routines. Locate physical edge availability zones supported by Vitess
Example Prompts for PlanetScale in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PlanetScale immediately.
"List all physical cloud regions currently exposed by the PlanetScale integration."
"We're starting a new feature. Fork testing branch from the main database 'store-backend'."
"Drop the specific 'staging-01' branch inside the 'web-portal' database."
Troubleshooting PlanetScale MCP Server with LlamaIndex
Common issues when connecting PlanetScale to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPlanetScale + LlamaIndex FAQ
Common questions about integrating PlanetScale 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?
Connect PlanetScale with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect PlanetScale to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.