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Brev

Official
by brevdev
GitHub
Python
3
  • Apple
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Brev MCP server

This is a MCP server implementation for Brev.

Configuration

The MCP server uses the Brev CLI's API access token and currently set org.

Follow the Brev documentation to download the CLI and login if you haven't already.

If you want to switch your Brev org, run brev set <org-name>

The CLI access token expires every hour. If you have any 403 errors, simply run brev ls to refresh the access token.

Quickstart

Setup repository locally

git clone git@github.com:brevdev/brev-mcp.git

Install uv

Follow the uv installation guide

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Add the following to your claude_desktop_config.json:

"mcpServers": { "brev_mcp": { "command": "uv", "args": [ "--directory", "<path-to-repo>", "run", "brev-mcp" ] } }

Development

Building and Publishing

To prepare the package for distribution:

  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

This will create source and wheel distributions in the dist/ directory.

  1. Publish to PyPI:
uv publish

Note: You'll need to set PyPI credentials via environment variables or command flags:

  • Token: --token or UV_PUBLISH_TOKEN
  • Or username/password: --username/UV_PUBLISH_USERNAME and --password/UV_PUBLISH_PASSWORD

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory /Users/tmontfort/Brev/repos/brev_mcp run brev-mcp

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

You must be authenticated.

A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

local-only server

The server can only run on the client's local machine because it depends on local resources.

Tools

Run, build, train, and deploy ML models on the cloud.

  1. Configuration
    1. Quickstart
      1. Setup repository locally
      2. Install uv
    2. Development
      1. Building and Publishing
      2. Debugging

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