Render MCP. Manage Services and Deployments from Chat.
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…and any MCP-compatible client
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Render MCP Server lets your AI client manage cloud infrastructure directly. You control deployments, suspend services to cut costs, provision new web apps from GitHub branches, and delete old staging environments—all via natural conversation.
It turns chat into a full DevOps command line.
What your AI agents can do
Create service
Builds and deploys a new service instance from a specified GitHub repository.
Delete service
Permanently removes an entire Render service—this action is irreversible.
Get deploy
Retrieves detailed information about a single, specific deployment run.
The agent lists all active services, databases, and scheduled jobs within your account using list_services.
You pause compute on inactive projects with suspend_service, stopping billing. Later, you wake them back up instantly with resume_service.
Trigger a manual deployment run using trigger_deploy to test hotfixes or push changes without waiting for the standard CI/CD schedule.
The agent builds out brand new services, connecting them directly to specific GitHub repositories via create_service.
You change the source code branch a service tracks using update_service_branch, redirecting it to a different repo path.
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Supported MCP Clients
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Render MCP Server: 10 Tools for Service Management
Manage service creation, deployment status, scaling operations, and resource cleanup with these ten dedicated tools.
019d75fecreate service
Builds and deploys a new service instance from a specified GitHub repository.
019d75fedelete service
Permanently removes an entire Render service—this action is irreversible.
019d75feget deploy
Retrieves detailed information about a single, specific deployment run.
019d75feget service
Fetches all current details for one particular Render service instance.
019d75felist deploys
Generates a list of recent deployment history for an existing service.
019d75felist services
Retrieves a complete inventory list of all services (web apps, databases, cron jobs) in your account.
019d75feresume service
Wakes up and reactivates a service that was previously suspended.
019d75fesuspend service
Stops a running service to prevent compute billing and halt execution immediately.
019d75fetrigger deploy
Forces an immediate, manual build and deployment run for a specified service.
019d75feupdate service branch
Changes the specific GitHub branch that a running service instance monitors for updates.
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 every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Render, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Listen up. This isn't just some fancy API hookup; this is your AI client talking directly to Render, letting you run full DevOps commands right from your chat window. You treat your whole cloud account like a live terminal session without ever having to log into the Render dashboard. It changes what natural conversation means for managing infrastructure.
Inventory and Status Check
To see everything running, use list_services to pull up a complete list of every single service—that includes web apps, databases, and cron jobs in your account. If you need granular details on one specific setup, run get_service for all current information about that instance. For historical context, list_deploys generates a full history of deployments for an existing service.
If you want the specifics on just one deployment attempt—like seeing what broke during a particular build—you use get_deploy.
Cost Management and Lifecycle Control
Managing costs is simple. You can stop compute billing instantly by calling suspend_service on any running project, which immediately halts execution. When you're done saving money, call resume_service, and the agent wakes up that suspended service right back up for action. The system also tracks what's happening now; use get_deploy to check the details of a specific deployment run.
Building and Updating Services
Need to build something from scratch? You can provision brand new services using create_service, connecting them directly to a specified GitHub repository. If that repo changes or you need it pointed at different code, use update_service_branch to change the source code branch that your service monitors for updates. To push out urgent fixes—like hotfixes—without waiting for the standard CI/CD cycle, simply trigger an immediate run with trigger_deploy.
You can also monitor all recent build attempts by running list_deploys, and if something is seriously wrong, you'll use get_service to check the current state of the instance.
Maintenance and Cleanup
When a service is obsolete or broken beyond repair, don't hesitate. Use natural language commands to permanently remove an entire Render service with delete_service; remember that action is irreversible because it deletes everything associated with that resource. You can also check on services you suspect are problematic using list_services first.
How Render MCP Works
- 1 Install the Render platform extension module into your MCP client.
- 2 Get your personal Render API Key from your Render Account Settings and input it securely into the connection configuration.
- 3 Chat with your AI using natural DevOps language, like: "List all web services, then suspend the one named 'dev-test-app'".
The bottom line is that you talk to your agent about infrastructure changes, and it executes the necessary API calls on Render for you.
Who Is Render MCP For?
This is for the SRE or Backend Developer who hates clicking through complex dashboards. If your job involves checking service status across multiple environments (staging, dev, prod) and cost management is a recurring pain point, you need this. It lets you manage infrastructure from the command line logic instead of relying on a UI.
They use list_services to audit all endpoints and then run suspend_service or resume_service quickly when environments need temporary downtime for maintenance.
They spin up quick background workers or private services using create_service, pointing to a specific GitHub branch just to test an API endpoint before full deployment.
They manage the full lifecycle by running list_deploys to check history, then executing trigger_deploy if they need a forced build validation run.
What Changes When You Connect
- Cut cloud costs instantly. Use
suspend_servicewhen a staging environment isn't needed, then hitresume_servicewhen testing starts up again. No need to manually toggle billing switches. - Control the entire build pipeline without UI clicks. If you spot an issue on GitHub, tell your agent to run
trigger_deploywith cache clearing instructions for a clean test run. - Fast service creation. Instead of going through multiple forms, just ask the agent to
create_service, pointing it at the required repo and branch immediately. - Full visibility in one prompt. Use
list_servicesfirst; you get status checks on web apps, databases, and cron jobs all listed out for review. - Flexible architecture changes. If a service needs to track code from a different source, use
update_service_branchinstead of manually reconfiguring the repo link.
Real-World Use Cases
Emergency Hotfix Deployment
The main site is down because of a bad commit. The developer tells their agent, 'List the service and then force a deployment.' The agent uses list_services to confirm the ID, then runs trigger_deploy. This bypasses slow CI/CD queues and gets the fix live immediately.
Cost Saving Audit
It's Friday afternoon. Nobody is touching the old staging environment. Instead of going to Render and remembering to delete it, the team asks their agent to list_services, identifies the 'staging-v1' instance, and runs suspend_service to stop all compute billing.
New Microservice Kickoff
A new feature requires a dedicated background worker. The developer just tells their agent, 'Build me a new service called X from the main repo.' The agent uses create_service, provisioning the resource and linking it to the needed GitHub source code in one go.
Environment Cleanup
The project finished its beta phase. Instead of leaving dozens of obsolete test databases running, the developer prompts their agent to find all services tagged 'beta' using list_services, and then issues a bulk command to delete_service.
The Tradeoffs
Using only basic chat commands
Just typing, 'Make my app better.' This does nothing. It's too vague and doesn't map to any actual infrastructure action.
→
You need to be specific. Instead of generic requests, use actions like: 'List all services with list_services, then suspend the one named staging-app using suspend_service.'
Ignoring deployment history
Relying on memory or old dashboards to figure out when the last successful build was. You might try restarting something that's already running fine.
→
Always check the past first. Run list_deploys using a service ID to see exactly when the last successful deployment occurred, giving you reliable data.
Over-relying on one tool
Using only get_service repeatedly without checking if the resource is actually running or needs updating.
→
Combine visibility with action. First, use list_services. Then, if needed, follow up with update_service_branch to ensure it's pointing at the current code.
When It Fits, When It Doesn't
Use this server if your primary pain point is moving between different infrastructure tools or dashboards. If you need full lifecycle control—from provisioning (create_service) and branching (update_service_branch) to immediate testing (trigger_deploy) and cost cutting (suspend_service)—this is essential. Don't use it just for viewing logs; if all you need is a read-only view of metrics, stick with your existing monitoring dashboards. If the problem is state management (e.g., 'I forgot to pause that test environment'), this tool gives you the direct API control needed to execute those state changes.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Render. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Managing cloud services shouldn't feel like clicking through five different tabs.
Right now, updating a microservice is a chore. You log into Render, find the service ID, navigate to its dashboard, check its connection settings in one tab, run it in another, and then maybe go back again just to see if the deployment succeeded. It's slow, and you lose context.
With this MCP server, you just tell your agent what needs doing. You can say, "List my web services, then suspend the old staging app." The agent handles all those clicks—the list, the check, the suspension—and reports back with a single status update.
Render MCP Server: Control deployments and scaling from chat.
Specific manual steps like manually clearing build caches or changing repository branches used to require logging into the service settings page. Now, you just tell your agent, "Run `trigger_deploy` on srv-backend88 and clear the cache." It handles the sequence.
It’s about eliminating friction. You get a fully automated command layer that keeps your focus on code, not platform UI.
Common Questions About Render MCP
How do I check if my services are running with list_services? +
You run list_services. This gives you a quick inventory of all endpoints, databases, and cron jobs in your Render account. It tells you the current status for everything at once.
Should I use suspend_service or delete_service? +
Use suspend_service if you plan to bring it back later; it just stops billing and execution. Only use delete_service when you are 100% sure the resource is obsolete because this action cannot be undone.
How do I force a new deployment with trigger_deploy? +
You instruct your agent to run trigger_deploy. This forces an immediate build and deployment cycle for a service, bypassing the standard scheduled CI/CD pipeline.
What if my repo changes? Should I use update_service_branch? +
Yes, if your team moves the code base to a new branch (like 'feature/v2'), you must run update_service_branch so that Render is tracking the right source code for the service.
What credentials must I provide when using `create_service`? +
You need a valid Render API Key for all service operations. You must obtain this key from your Render Account Settings under the API Keys section and pass it to the connection configuration. Without this secure key, the agent cannot authenticate or provision resources.
How do I check if a specific deployment failed using `get_deploy`? +
Check the 'status' field returned by get_deploy. If the status isn't 'Successful' or 'Live', it indicates an error. The response payload will provide detailed failure logs that explain exactly why the build broke.
If I used `suspend_service`, how do I bring it back online using `resume_service`? +
Running resume_service immediately restores service availability and compute resources. The system will restart the service, moving it from a billing-paused state to fully active operations. Be aware that resumed services may require a brief startup period.
What information do I need before running `create_service`? +
You must provide three core pieces of data: the service type (e.g., web service, database), the name you want for the resource, and the specific GitHub repository link. These inputs tell the agent exactly what to build.
Can the AI clear the cache when triggering a deploy? +
Yes, absolutely. The tool trigger_deploy incorporates an optional variable explicitly created for cache management. You can command the agent: "Redeploy the web app named Node-Backend and bypass rendering cache."
Which type of new services can the AI deploy using `create_service`? +
The MCP can provision and launch exactly three core resource forms utilizing GitHub repos: standard web services (web_service), private network-locked processes (private_service), and asynchronous task handlers (background_worker).
Warning: Is there a confirmation before using `delete_service`? +
Since natural language agents can occasionally misinterpret parameters, invoking the text request explicitly will route straight to the Render API resulting in instantaneous destruction. Please ensure absolute clarity when pointing the AI logic toward deletion operations.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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