developer-tools

Podman

manusa

by manusa

Integrate with Podman for seamless container creation, management, and orchestration in automated DevOps and microservic

Integrates with Podman's API to enable container creation, management, and orchestration for automated DevOps workflows and microservices architecture deployment.

github stars

60

0 commentsdiscussion

Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Works with both Podman and DockerAvailable via npm and PyPI

best for

  • / DevOps engineers managing container workflows
  • / Developers testing containerized applications
  • / System administrators orchestrating microservices
  • / CI/CD pipeline automation

capabilities

  • / Create and start containers
  • / Build container images from Dockerfiles
  • / Monitor container status and logs
  • / Manage container networks and volumes
  • / Pull and push container images
  • / Execute commands inside running containers

what it does

Provides direct API access to Podman for container management through conversational interfaces. Enables creation, monitoring, and orchestration of containers and images without switching between tools.

about

Podman is a community-built MCP server published by manusa that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Podman for seamless container creation, management, and orchestration in automated DevOps and microservic It is categorized under developer tools.

how to install

You can install Podman in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

Apache-2.0

Podman is released under the Apache-2.0 license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

Podman MCP Server

GitHub License npm PyPI - Version GitHub release (latest SemVer) Build

✨ Features | 🚀 Getting Started | 📚 Documentation | 🎥 Demos | ⚙️ Configuration | 🛠️ Tools | 🧑‍💻 Development

✨ Features <a id="features"></a>

A powerful and flexible MCP server for container runtimes supporting Podman and Docker.

🚀 Getting Started <a id="getting-started"></a>

Claude Desktop

Using npx

If you have npm installed, this is the fastest way to get started with podman-mcp-server on Claude Desktop.

Open your claude_desktop_config.json and add the mcp server to the list of mcpServers:

{
  "mcpServers": {
    "podman": {
      "command": "npx",
      "args": [
        "-y",
        "podman-mcp-server@latest"
      ]
    }
  }
}

VS Code / VS Code Insiders

Install the Podman MCP server by clicking one of the following links:

<img src="https://img.shields.io/badge/VS_Code-VS_Code?style=flat-square&label=Install%20Server&color=0098FF" alt="Install in VS Code"> <img alt="Install in VS Code Insiders" src="https://img.shields.io/badge/VS_Code_Insiders-VS_Code_Insiders?style=flat-square&label=Install%20Server&color=24bfa5">

Alternatively, you can install the extension manually by running the following command:

# For VS Code
code --add-mcp '{"name":"podman","command":"npx","args":["-y","podman-mcp-server@latest"]}'
# For VS Code Insiders
code-insiders --add-mcp '{"name":"podman","command":"npx","args":["-y","podman-mcp-server@latest"]}'

Goose CLI

Goose CLI is the easiest (and cheapest) way to get rolling with artificial intelligence (AI) agents.

Using npm

If you have npm installed, this is the fastest way to get started with podman-mcp-server.

Open your goose config.yaml and add the mcp server to the list of mcpServers:

extensions:
  podman:
    command: npx
    args:
      - -y
      - podman-mcp-server@latest

📚 Documentation <a id="documentation"></a>

For detailed setup guides, configuration reference, and feature specifications, see the Documentation.

🎥 Demos <a id="demos"></a>

⚙️ Configuration <a id="configuration"></a>

The Podman MCP server can be configured using command line (CLI) arguments.

You can run the CLI executable either by using npx or by downloading the latest release binary.

# Run the Podman MCP server using npx (in case you have npm installed)
npx podman-mcp-server@latest --help
# Run the Podman MCP server using the latest release binary
./podman-mcp-server --help

Configuration Options

OptionDescription
--port, -pStarts the MCP server in HTTP mode with Streamable HTTP at /mcp and SSE at /sse endpoints.
--output-format, -oOutput format for list commands: text (default, human-readable table) or json.
--podman-implPodman implementation to use. Auto-detects if not specified.
--sse-portDeprecated. Use --port instead. Starts the MCP server in SSE-only mode.
--sse-base-urlDeprecated. SSE public base URL to use when sending the endpoint message.

Transport Modes

The server supports multiple transport modes:

  1. STDIO mode (default) - Communicates via standard input/output
  2. HTTP mode (--port) - Modern HTTP transport with both Streamable HTTP and SSE endpoints
  3. SSE-only mode (--sse-port) - Legacy Server-Sent Events transport (deprecated)
# Start HTTP server on port 8080 (Streamable HTTP at /mcp and SSE at /sse)
podman-mcp-server --port 8080

# Legacy SSE-only server on port 8080 (deprecated, use --port instead)
podman-mcp-server --sse-port 8080

Podman Implementations

The server supports multiple Podman backend implementations:

ImplementationDescriptionPriority
apiPodman REST API via Unix socket100 (preferred)
cliPodman/Docker CLI wrapper50 (fallback)

By default, the server auto-detects the best available implementation. The api implementation is preferred when a Podman socket is available, otherwise the cli implementation is used as a fallback.

Use the --podman-impl flag to force a specific implementation:

# Force CLI implementation
podman-mcp-server --podman-impl=cli

# Force API implementation (requires Podman socket)
podman-mcp-server --podman-impl=api

The api implementation communicates directly with the Podman REST API via Unix socket, while the cli implementation shells out to the podman or docker binary.

🛠️ Tools <a id="tools"></a>

<!-- AVAILABLE-TOOLS-START --> <details> <summary>Container</summary>
  • container_inspect - Displays the low-level information and configuration of a Docker or Podman container with the specified container ID or name

    • name (string) (required) - Docker or Podman container ID or name to display the information
  • container_list - Prints out information about the running Docker or Podman containers

  • container_logs - Displays the logs of a Docker or Podman container with the specified container ID or name

    • name (string) (required) - Docker or Podman container ID or name to display the logs
  • container_remove - Removes a Docker or Podman container with the specified container ID or name (rm)

    • name (string) (required) - Docker or Podman container ID or name to remove
  • container_run - Runs a Docker or Podman container with the specified image name

    • environment (array) - Environment variables to set in the container. Format: <key>=<value>. Example: FOO=bar. (Optional, add only to set environment variables)
    • imageName (string) (required) - Docker or Podman container image name to run
    • ports (array) - Port mappings to expose on the host. Format: <hostPort>:<containerPort>. Example: 8080:80. (Optional, add only to expose ports)
  • container_stop - Stops a Docker or Podman running container with the specified container ID or name

    • name (string) (required) - Docker or Podman container ID or name to stop
</details> <details> <summary>Image</summary>
  • image_build - Build a Docker or Podman image from a Dockerfile, Podmanfile, or Containerfile

    • containerFile (string) (required) - The absolute path to the Dockerfile, Podmanfile, or Containerfile to build the image from
    • imageName (string) - Specifies the name which is assigned to the resulting image if the build process completes successfully (--tag, -t)
  • image_list - List the Docker or Podman images on the local machine

  • image_pull - Copies (pulls) a Docker or Podman container image from a registry onto the local machine storage

    • imageName (string) (required) - Docker or Podman container image name to pull
  • image_push - Pushes a Docker or Podman container image, manifest list or image index from local machine storage to a registry

    • imageName (string) (required) - Docker or Podman container image name to push
  • image_remove - Removes a Docker or Podman image from the local machine storage

    • imageName (string) (required) - Docker or Podman container image name to remove
</details> <details> <summary>Network</summary>
  • network_list - List all the available Docker or Podman networks
</details> <details> <summary>Volume</summary>
  • volume_list - List all the available Docker or Podman volumes
</details> <!-- AVAILABLE-TOOLS-END -->

🧑‍💻 Development <a id="development"></a>

Running with mcp-inspector

Compile the project and run the Podman MCP server with mcp-inspector to inspect the MCP server.

# Compile the project
make build
# Run the Podman MCP server with mcp-inspector
npx @modelcontextprotocol/inspector@latest $(pwd)/podman-mcp-server

mcp-name: io.github.manusa/podman-mcp-server

FAQ

What is the Podman MCP server?
Podman is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Podman?
This profile displays 65 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.665 reviews
  • Sakura Taylor· Dec 28, 2024

    We wired Podman into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Pratham Ware· Dec 24, 2024

    Podman has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Jin Garcia· Dec 24, 2024

    We evaluated Podman against two servers with overlapping tools; this profile had the clearer scope statement.

  • Hana Abbas· Dec 24, 2024

    I recommend Podman for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Hiroshi Martinez· Dec 20, 2024

    Podman reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Sakura Bansal· Dec 20, 2024

    According to our notes, Podman benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Jin Martin· Dec 16, 2024

    Podman is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Hiroshi Perez· Nov 19, 2024

    Strong directory entry: Podman surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Sakshi Patil· Nov 15, 2024

    According to our notes, Podman benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Arya Rahman· Nov 15, 2024

    Podman is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

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