MCP server providing containerized conda environments via Podman
Project description
envhub
MCP server providing containerized conda environments via Podman for AI agents.
Prerequisites
- Python >= 3.10
- Podman
Install
pip install envhub-mcp
Build Container Image
# Find the docker directory
python -c "import envhub; print(envhub.__file__)"
# Build (from the package's docker/ directory)
podman build -t envhub:latest <path-to-envhub>/docker/
Usage
envhub --workdir /path/to/work
Multiple working directories:
envhub --workdir /path/a --workdir /path/b
MCP Tools
| Tool | Description |
|---|---|
run(command, env, timeout) |
Execute shell commands in a conda environment |
list_envs() |
List available conda environments |
list_workdirs() |
List mounted working directories |
Environments
- default — Data analysis + visualization (pandas, scikit-learn, matplotlib, plotly, R/ggplot2)
- plot — Professional plotting (matplotlib, seaborn, plotly, plotnine, R/ggplot2, cairo)
License
MIT
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