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
1 maintainer
Metadata
1 maintainer
Release files for envhub-mcp 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| envhub_mcp-0.1.0.tar.gz | 13.2 MB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| envhub_mcp-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.3 MB
Release files / envhub_mcp-0.1.0.tar.gz
| Download URL | envhub_mcp-0.1.0.tar.gz |
|---|---|
| Size | 13.2 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / envhub_mcp-0.1.0-py3-none-any.whl
| Download URL | envhub_mcp-0.1.0-py3-none-any.whl |
|---|---|
| Size | 13.2 MB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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