A podman ubuntu 24.04 container that serves a MCP server; with file, code execution, bash shell, knowledgebase, and more.
A podman ubuntu 24.04 container that serves a MCP server; with file, code execution, bash shell, knowledgebase, and more.
A secure, container-based implementation of the Model Context Protocol (MCP) for executing tools on behalf of large language models.
Container-MCP provides a sandboxed environment for safely executing code, running commands, accessing files, and performing web operations requested by large language models. It implements the MCP protocol to expose these capabilities as tools that can be discovered and called by AI systems in a secure manner.
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Check the linked publisher documentation and your connected client's tool list. Names and availability can vary with the server version and account permissions.
| Tool | What it does |
|---|---|
| system_run_command | |
| system_run_python | |
| system_env_var | |
| health_check | |
| fs_read | |
| fs_write | |
| fs_list | |
| fs_delete | |
| fs_move | |
| fs_apply_diff | Applies a unified diff patch to a file in the sandbox filesystem. |
| web_search | |
| web_scrape | |
| web_browse | Note: web_browse requires Playwright browser binaries. Outside the container, install them with python -m playwright install chromium (or playwright install). |
| kb_create_document | Creates a new document in the knowledge base with optional metadata and content. |
| Variable | Description | Required |
|---|---|---|
| MCP_PORT | Configuration value read at startup. | Optional |
| SANDBOX_ROOT | Configuration value read at startup. | Optional |
Retrieve public web pages as readable Markdown with the reference Fetch MCP server, including pagination for longer responses.
Use Firecrawl MCP to retrieve website content and extract information for research through Firecrawl’s API-backed web tools.
The original Chromium automation reference server — simple, screenshot-driven browser control.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Automates browsers and APIs, enabling Large Language Models (LLMs) to interact with web pages for tasks like screenshot capture and JavaScript
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.