Render MCP Server

Deploy to Render.com directly through AI assistants.

This MCP (Model Context Protocol) server allows AI assistants like Claude to interact with the Render API, enabling deployment and management of services on Render.com.

Features

This server covers everything Render's official MCP server does plus mutating operations their server intentionally omits (triggering deploys, deleting resources, managing custom domains, restarting, and cancelling deploys).

Services

• List all services and get details of a specific service

• Create services: generic create_service plus typed create_web_service, create_static_site, and create_cron_job

• Deploy, restart, and delete services

• Manage environment variables and custom domains

Deploys

• Get deployment history, get a single deploy, and cancel an in-progress deploy

Workspaces

• List workspaces, get workspace details, and select a default workspace so create/logs/metrics tools don't need an ownerId each call

Observability

• List and filter logs (list_logs) and enumerate log label values

• Fetch performance metrics (get_metrics): CPU, memory, HTTP requests/latency, bandwidth, instance count, active connections

Datastores

• Postgres: list, get, create, and run read-only SQL queries (query_render_postgres)

• Key Value (Redis): list, get, and create instances

Note on query_render_postgres: this tool connects to your database using the pg driver and enforces read-only access (single statement, SELECT/WITH/ EXPLAIN/SHOW only, run inside a READ ONLY transaction). Run npm install after installing the package to ensure the pg dependency is present.

Related MCP server: Railway MCP Server

Installation

npm install -g @niyogi/render-mcp

Configuration

1. Get your Render API key from Render Dashboard

2. Configure the MCP server with your key:

node bin/render-mcp.js configure --api-key=YOUR_API_KEY

Alternatively, you can run node bin/render-mcp.js configure without the --api-key flag to be prompted for your API key.

Usage

Starting the Server

node bin/render-mcp.js start

Checking Configuration

node bin/render-mcp.js config

Running Diagnostics

node bin/render-mcp.js doctor

Note: If you've installed the package globally, you can also use the shorter commands:

render-mcp start
render-mcp config
render-mcp doctor

Using with Different AI Assistants

Using with Claude Code

The quickest way is the claude mcp add CLI, which registers the server via npx (no global install needed):

claude mcp add render -e RENDER_API_KEY=YOUR_API_KEY -- npx -y @niyogi/render-mcp start

By default this adds the server to the current project. Use a scope flag to change that:

• -s user — available across all your projects

• -s project — shared with your team via a checked-in .mcp.json

• -s local (default) — just this project, only for you

Alternatively, add it manually to your MCP config (.mcp.json in the project root, or ~/.claude.json for user scope):

{
  "mcpServers": {
    "render": {
      "command": "npx",
      "args": ["-y", "@niyogi/render-mcp", "start"],
      "env": {
        "RENDER_API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Instead of passing the key via env, you can store it once with npx @niyogi/render-mcp configure (saved to ~/.render-mcp/config.json) and omit the env block.

Then verify the connection inside Claude Code with the /mcp command — render should appear as connected and its tools listed.

Using with Cline

1. Add the following to your Cline MCP settings file:

   {
     "mcpServers": {
       "render": {
         "command": "node",
         "args": ["/path/to/render-mcp/bin/render-mcp.js", "start"],
         "env": {
           "RENDER_API_KEY": "your-render-api-key"
         },
         "disabled": false,
         "autoApprove": []
       }
     }
   }

2. Restart Cline for the changes to take effect

3. You can now interact with Render through Claude:

   Claude, please deploy my web service to Render

Using with Windsurf/Cursor

1. Install the render-mcp package:

   npm install -g @niyogi/render-mcp

2. Configure your API key:

   node bin/render-mcp.js configure --api-key=YOUR_API_KEY

3. Start the MCP server in a separate terminal:

   node bin/render-mcp.js start

4. In Windsurf/Cursor settings, add the Render MCP server:

   • Server Name: render

   • Server Type: stdio

   • Command: node

   • Arguments: ["/path/to/render-mcp/bin/render-mcp.js", "start"]

5. You can now use the Render commands in your AI assistant

Using with Claude API Integrations

For custom applications using Claude's API directly:

1. Ensure the render-mcp server is running:

   node bin/render-mcp.js start

2. In your application, when sending messages to Claude via the API, include the MCP server connections in your request:

   {
     "mcpConnections": [
       {
         "name": "render",
         "transport": {
           "type": "stdio",
           "command": "node",
           "args": ["/path/to/render-mcp/bin/render-mcp.js", "start"]
         }
       }
     ]
   }

3. Claude will now be able to interact with your Render MCP server

Example Prompts

Here are some example prompts you can use with Claude once the MCP server is connected:

• "List all my services on Render"

• "Deploy my web service with ID srv-123456"

• "Create a new static site on Render from my GitHub repo"

• "Show me the deployment history for my service"

• "Add an environment variable to my service"

• "Add a custom domain to my service"

• "List my Render workspaces and select the team one"

• "Show me the error logs for srv-123456 from the last hour"

• "What's the CPU and memory usage for srv-123456?"

• "Query my Render Postgres: SELECT count(*) FROM users"

• "Restart my service srv-123456"

• "Create a Postgres database and a Redis instance for my app"

Development

Building from Source

git clone https://github.com/niyogi/render-mcp.git
cd render-mcp
npm install
npm run build

Running Tests

npm test

License

MIT