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Safely manage Python environments and dependencies within a project workspace.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "dev-env-mcp" yet — see the docs or source repo.
Create a new Python virtual environment in the current project workspace and report the environment path and Python version.
Returns the virtual environment creation result, plus the environment location and Python version.
Install requests and pandas in this project's virtual environment, then list the installation result and currently installed versions.
Returns dependency installation status and a list of installed package versions.
Freeze the dependencies in the current virtual environment and generate a package version list suitable for requirements.txt.
Outputs a normalized dependency version list for reproducing the project environment.
Secure file and directory operations for autonomous AI development workflows.
Manage Conda environments and packages through MCP for streamlined setup workflows.
Enables secure local development with files, shell, editing, and persistent sessions.
Scaffold and manage MCP-compatible server projects with environments and tooling.
Perform file tasks, manage npm packages, and check configuration security via MCP.
Use one MCP server for filesystem, database, web, and system operations.