Sandboxed bash execution MCP server for AI agents, using an in-memory virtual filesystem overlay to prevent real filesystem damage, with configurable network access, timeouts, and optional Python/JS runtimes.
Enables AI agents to create and manage personalized 30-day study courses, including web research with source validation, daily lessons, quizzes, and progress tracking through 12 MCP tools.
An MCP server that integrates with the Just command runner, enabling AI assistants to discover, execute, and introspect Justfile recipes through the MCP protocol.
myopic is a code-review MCP server that reviews GitLab/GitHub merge requests against the whole codebase, not just the diff, analyzing blast radius and conventions.
DBeast MCP is a robust, security-first Model Context Protocol server designed to empower AI coding assistants (like Claude Desktop) with deep PostgreSQL database insights. Instead of just running basic SQL queries, DBeast equips your AI agent with tools to inspect active locks, audit slow queries, analyze table schemas, and safely generate index recommendations in real-time. Built specifically for
Run DeepSeek as a real sub-agent inside Claude Code / Codex CLI — not just a single LLM call. DeepSeek gets its own 7-tool agent loop (Read/Write/Edit/Bash/Glob/Grep/NotebookEdit) inside a sandboxed workspace.
A local MCP server that provides semantic code search for Python codebases using tree-sitter for chunking and LanceDB for vector storage. It enables natural language queries to find relevant code snippets based on meaning rather than just text matching.
A production-style MCP server that lets Claude Desktop search your local documents using TF-IDF keyword search. No heavy frameworks, just plain Python and the official MCP SDK.
Enables AI to autonomously play the TokenLife text-based life simulation game, from birth to death, experiencing events like censorship and era changes, and sending letters to the user. Users just ask the AI to start and it plays itself.
Share the HTML your agent just wrote as a tracked link in one MCP call, then read back who opened it and which sections they read. Open source, AGPL-3.0.
Provides a clean interface for AI agents to search arXiv papers, fetch metadata, read full text, and download PDFs/source, with rate-limiting, on-disk caching, and session pinning to avoid duplicate fetches.