MCP server providing containerized conda environments via Podman
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"envhub-mcp","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# envhub MCP server providing containerized conda environments via Podman for AI agents. ## Prerequisites - Python >= 3.10 - [Podman](https://podman.io/docs/installation) ## Install ```bash pip install envhub-mcp ``` ## Build Container Image ```bash # 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 ```bash envhub --workdir /path/to/work ``` Multiple working directories: ```bash 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
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.