PlanetScale MCP for AI Agents. Manage database schemas and global infrastructure from your IDE.
PlanetScale MCP lets your AI agent manage serverless MySQL databases and complex schema structures directly from your IDE. You can provision new global database instances, list regional endpoints, or create temporary, isolated test branches by simply asking your client. This is essential for full-stack developers who need to safely version schemas—treating the entire database setup like source code branching—without leaving their development environment.
Give Claude and any AI agent real-world access
Discover all physical cloud edge zones and core organizational identifiers required for setting up databases.
Create brand new, scalable serverless MySQL instances ready to accept definitions and data loads.
Generate temporary, empty schema branches from a production database for consequence-free feature testing.
List or delete existing development branches to maintain clean and predictable data schemas.
Retrieve the core setup details for any specific MySQL cluster you manage.
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What AI agents can do with PlanetScale: Database & Branch Ops (10 Tools)
These tools give your agent the power to audit infrastructure, create new clusters, version schemas, and perform destructive actions on PlanetScale MySQL databases.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using PlanetScale MCPCreate Branch
Creates an isolated, empty schema copy of a parent database for secure testing without affecting live data.
Create Database
Provisions a new, scalable serverless MySQL cluster instance ready for your defined...
Delete Branch
Permanently removes an old or unused development branch from the database structure.
Delete Database
Irreversibly destroys an entire PlanetScale MySQL construct, wiping all associated...
Get Branch
Retrieves the necessary access hostnames for a specific database branch so your code...
Get Database
Analyzes and returns the core configuration details of any targeted MySQL cluster logic.
List Branches
Provides a list of all development database branches currently existing in the system, mirroring production architectures.
List Databases
Retrieves details for all high-availability MySQL databases distributed across your...
List Organizations
Lists the foundational root identifiers needed to manage and connect to any...
List Regions
Locates all supported physical cloud edge zones, helping you plan where to deploy...
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with PlanetScale, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
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VINKIUS INFRASTRUCTURE
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Managed infra
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Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
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EU data residency
Token Compression
~60% cost reduction
Database schema versioning used to be a nightmare of console clicks. Solved with Vinkius AI Gateway
Today, updating a database structure means logging into the web portal, navigating through multiple tabs, manually executing DDL statements on development clusters, and hoping you didn't accidentally target production. The process is slow, error-prone, and requires constant context switching.
With this MCP, that friction vanishes. You tell your agent to create an isolated test branch using `create_branch`. It handles the entire provisioning step in the background; you just get a clean, empty environment ready for testing strings.
The PlanetScale MCP gives you control over database structures.
You no longer have to manually remember which staging environments are active or how many test branches exist. You can run `list_branches` and get a definitive, current list of every single schema fork, knowing exactly what's running and why.
It’s not just about listing them; you can tell your agent to use `delete_branch`, executing the purge command instantly. The entire process is now managed by natural language conversation.
What your AI can actually do with this
Managing complex data infrastructure used to mean jumping between a console dashboard and local scripts. Now, you can let your AI agent handle it all. You tell your client what needs fixing—whether that's spinning up a temporary copy of the main schema for testing or auditing which physical cloud regions are available—and your MCP executes the commands.
This connector lets you manage entire database lifecycles: provisioning brand new clusters, checking existing configurations, and handling complex branching workflows instantly. If you rely on reliable data infrastructure management, connecting this through Vinkius gives you a centralized place to run these advanced commands across any compatible AI client.
019d75f6-d04b-73bc-9087-6379bc83ba39 Here's how it actually works
The bottom line is you tell your agent what infrastructure change you need, and it handles the complex backend steps to make it happen safely.
First, ensure your AI client has subscribed to this MCP and that you've provided an active PlanetScale Service Token.
Next, use natural language prompts in your IDE. Your agent translates the request (e.g., 'Create a test branch for Feature X') into the necessary API calls.
The system executes the command against PlanetScale and returns the result—whether it's a list of available regions or confirmation that the new schema branch exists.
Who is this actually for?
This MCP is for Platform Architects who are sick of manually checking global endpoints. It's for Full-stack Developers who can't afford a broken staging environment. And it’s for DevOps Engineers who need to automatically clean up stale, unused database branches after every deployment.
Automates the cleanup of obsolete testing environments by instructing your agent to delete specific schema branches or entire databases.
Spins up ephemeral, isolated database branches when starting a new feature so they can test code without risking the main production environment.
Audits global deployment footprints by listing all organizational identifiers and available physical cloud regions to plan multi-region expansion.
What Changes When You Connect
Test schema changes safely. By using the create_branch tool, you get an empty copy of production data's structure for testing without ever touching the live main branch.
Automate cleanup tasks. When a feature is done, use delete_branch to purge obsolete test environments immediately, keeping your database clean and manageable.
Scale globally with confidence. Run list_regions to audit physical edge locations. This lets you plan where to provision new databases for optimal latency targets.
Speed up provisioning. Instead of manual console clicks, simply ask your agent to use create_database to spin up a fully configured serverless MySQL instance in natural language.
Know your structure instantly. If you need to check the details of an existing cluster, running get_database gives you the core configuration without needing to navigate complex web consoles.
See it in action
Feature Development Requires Isolation
A developer needs to test a database migration for Feature X. They ask their agent to run create_branch off the main production schema, getting an isolated environment without duplicating gigabytes of live user data.
Preparing for Multi-Region Launch
A platform architect plans expansion into Europe. The agent runs list_regions, confirming that 'eu-west' endpoints are available, allowing the architect to proceed with provisioning new clusters immediately.
Database Cleanup After Deployment
After a major release, several temporary test branches remain active. The devops engineer asks the agent to use delete_branch on all identified stale schema forks, clearing clutter and reducing overhead.
The honest tradeoffs
What to watch out for, and the recommended way to handle each one.
Deleting data manually in production
A user tries to delete test data by running a generic 'drop table' command directly against the main database without knowing if it hits a live schema.
Always create an isolated test environment first. Ask your agent to run create_branch from main. Then, perform all destructive actions on that temporary branch before attempting deployment.
Assuming global connectivity
A developer assumes they can provision a database in a new country because it looks logical, only to find out the physical edge isn't supported.
Before provisioning anywhere, run list_regions. This confirms that your agent can locate and use every available physical cloud provider edge.
Trying to manually track schema versions
A team struggles with which branch is the definitive source of truth for a given feature set, leading to conflicts.
Use list_branches and get_branch. This provides a clear inventory of every existing development path, ensuring everyone knows exactly what schema version they're working from.
When It Fits, When It Doesn't
Use this MCP if your core problem is managing the lifecycle of complex database schemas—specifically needing to branch, test against temporary copies, and clean up old versions. You need tools that treat your data structure like code. Don't use this if you just need simple read/write access; those are standard credentials. Also, don't use it if your goal is only to manage user accounts or messaging workflows—this is purely for the underlying database infrastructure. If you simply need to check which databases exist, run list_databases. If you need to audit global availability zones, run list_regions.
Questions you might have
How do I check which physical regions PlanetScale supports using the PlanetScale MCP? +
You run list_regions. This tool immediately returns a full list of all supported cloud edge zones, allowing you to audit your global deployment footprint before starting any new project.
Can I test a schema change without affecting the live database using PlanetScale MCP? +
Yes. Use create_branch to fork an empty, compliant copy of the main schema. This lets you run all your tests on the temporary branch without risking production data.
What is the difference between list_databases and list_branches in PlanetScale MCP? +
list_databases shows every major, high-availability MySQL cluster. list_branches specifically lists all the temporary development schema versions you've created for testing purposes.
If I delete a database using PlanetScale MCP, is it recoverable? +
No. The delete_database tool is permanent and irreversible; it will wipe terabytes of records globally. Always confirm the target ID before running this command.
How do I get connection details for a temporary test branch using PlanetScale MCP? +
Run get_branch. This tool analyzes your schema version and returns all the necessary access hostnames required for your application to connect to that specific development branch.