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securityF
license-
qualityEnables LLMs to perform statistical analysis and generate ML predictions on user data from databases or CSV files through a Model Context Protocol server.
Last updated -
Python
The Brev MCP server allows you to manage cloud infrastructure for workspaces and instances across multiple providers (AWS, GCP, Azure, Crusoe, Lambda Labs, Fluidstack, Launchpad, Akash, GCP Alpha).
You can:
- Retrieve available instance types for specific cloud providers
- Create workspaces by specifying name, instance type, and cloud provider
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:
Development
Building and Publishing
To prepare the package for distribution:
- Sync dependencies and update lockfile:
- Build package distributions:
This will create source and wheel distributions in the dist/ directory.
- Publish to PyPI:
Note: You'll need to set PyPI credentials via environment variables or command flags:
- Token:
--tokenorUV_PUBLISH_TOKEN - Or username/password:
--username/UV_PUBLISH_USERNAMEand--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:
Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.
local-only server
The server can only run on the client's local machine because it depends on local resources.
Run, build, train, and deploy ML models on the cloud.
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