Run Podman containers with safety measures, blocking dangerous flags during operations like run, stop, pull, and inspect.
MIT
203,628 tools. Last updated 2026-06-14 19:34
"Podman" matching MCP tools:
- Route a bash command to a specific cluster node (Linux x86_64, macOS ARM64) for architecture-specific execution or debugging.MIT
- Check Docker/Podman and GPU availability to verify platform readiness for vLLM operations across Linux, macOS, and Windows systems.Apache 2.0
- View running Podman containers or display all containers when needed to manage containerized applications and monitor container status.
- Inspect container details by name or ID to view configuration, status, and runtime information for containerized applications managed through Podman.
- Stop a running Podman container by name or ID. Configure timeout period before automatic termination to manage container lifecycle operations.
Matching MCP Servers
- FlicenseAqualityDmaintenanceEnables AI tools to manage containerized applications through Podman, supporting container lifecycle operations, command execution, log viewing, image management, and resource monitoring. Features automatic network discovery for seamless integration with MCP Discovery Hub.Last updated12
- AlicenseBqualityCmaintenanceExposes vLLM capabilities to AI assistants, enabling chat completions, model management, and platform-aware container control with automatic detection of Docker/Podman and GPU availability across Linux, macOS, and Windows.Last updated126Apache 2.0
- Monitor container resource usage statistics including CPU, memory, and network metrics to track performance and identify resource bottlenecks in Podman containers.
- Pull container images from registries to enable deployment of containerized applications using Podman. This tool retrieves specified images for container management operations.
- Restart a Podman container to apply configuration changes, recover from issues, or refresh the container state using the container name or ID.
- Retrieve container logs to monitor application output and troubleshoot issues. Specify container name/ID and optionally set the number of log lines to display from the end.
- Execute commands inside containers to run applications, perform maintenance tasks, or troubleshoot running processes within your containerized environment.
- Display available container images from Podman to manage your containerized applications and perform image-related operations.
- Start a stopped container to resume its operations. Specify the container name or ID to initiate the container lifecycle process.
- Start a new container with specified image, ports, volumes, and environment variables. Configure container settings and run applications in isolated environments.
- Delete a container from the Podman MCP Server by specifying its name or ID. Use the force option to remove running containers.
- Deploys an app to a VM and exposes it at a public https://<name>-<id>.redu.cloud URL (a short random suffix is appended; pass an explicit `dname` for a stable, predictable URL). The container is built ON the VM — no local Docker/podman needed. PREREQS — run check_deploy_prerequisites first: it auto-selects your network_id + keypair_name (and returns a recipe to mint a keypair if you have none). Pass those two ids here. PORT: pass the port the app actually listens on (plan_deploy detects it / Dockerfile EXPOSE) — redu health-probes that exact port, so a wrong/omitted port (defaults to 3000) fails a non-3000 app (e.g. a static nginx app listens on 80 → pass 80). TWO source modes: (1) GIT — pass `repo` (public; private repos also need git_token). (2) UPLOAD — call prepare_upload first to tar + POST your LOCAL working dir, then pass the returned `source_token` (no git, no PAT; use this for uncommitted code, a fixed clone of a repo you don't own, or private code). The source needs a Containerfile/Dockerfile; redu auto-finds one in common subfolders (Docker/, scripts/, packaging/…) and builds with the repo root as context — for a repo with MULTIPLE Dockerfiles pass `dockerfile`+`context` to pick the right one. If it has NONE, pass dockerfile_content (the one plan_deploy generated) or include a Dockerfile in the uploaded tarball. To wire a DB, pass `database` (auto-injects the connection env + DATABASE_URL — zero setup): `database:'single_vm'` puts Postgres ON the app VM (cheapest; data dies if the VM is replaced); `database:'managed'` provisions a SEPARATE managed-DB VM on the same private network and wires it automatically (data PERSISTS across redeploys; reused on a same-name redeploy) — you do NOT call create_database/create_relational_database for this. Choose the engine with `db_engine` ('postgres' default → PG* env; 'mysql'/'mariadb' → MYSQL_* env + mysql:// URL, for WordPress/Matomo/LAMP apps; mysql/mariadb require database:'managed'). redu also injects APP_URL/PUBLIC_URL (= the app's public URL) into its env, so apps that need their own URL get it (map an app-specific var like BASE_URL to PUBLIC_URL if needed). Build+provision takes ~3-6 min (a bit longer for managed, which also brings up the DB VM); poll list_deployments or get_deployment until status='ready'. On 'build_failed'/'error', call get_deployment(id) to read build_log. ALWAYS run plan_deploy first and confirm the plan + cost with the user before deploying.Connector
- OPTIONAL preflight: returns a podman-compose.yml + .env so the user can run the app (and a throwaway local Postgres) on THEIR machine before deploying to redu — to see it run / sanity-check the container. Requires local podman/podman-compose. It's a suggestion, not a gate — skip it and go straight to deploy_app any time. Honest caveat: the local Postgres is NOT the managed Postgres, so a green local run does not prove the prod DB wiring.Connector