"AI Agent Coder with Awareness of Various SDKs" matching MCP servers:

Enables code execution in isolated Docker containers with persistent IPython, Node.js, or R kernels, supporting file import/export and cross-session transfers via MCP tools.

Provides workspace-scoped file operations and command execution tools for building Cursor-style agents, along with system prompts and tool definitions for LLM integration.

Delegates coding and read-only exploration tasks to the Antigravity CLI as blocking headless runs, mirroring live progress to on-disk stream transcripts and diffing git status to report touched files and out-of-scope edits. Supports resumable conversations, per-call model/agent/effort overrides, idle and hard timeouts, and discovery of available models and agent profiles.

Encoding-aware, indentation-smart file tools for AI coding agents. 20+ tools including read/edit with automatic encoding detection, smart indentation conversion, SSH, SFTP, process management, and system utilities.

Enables running multiple AI CLI tools (Claude, Codex, Gemini, Forge, OpenCode) as background processes with automatic permission handling, allowing LLMs to execute async multitasking and combine different AI models.

Enables MCP-based communication with existing interactive terminal channels, such as shells or agent CLIs, by listing, reading, writing text, and sending control signals to tmux sessions without creating or managing them.

The Problem Modern distributed systems and AI agents require robust operational scaffolding. Simple CRUD apps or mock loops fail when subjected to real-world edge cases, asynchronous boundaries, and security constraints. The Solution An isolated execution platform for AI agents. An agent is given a broken service and a bounded toolset inside a disposable sandbox. Every command passes throug

Enables MCP clients to drive developer workflows through APIs and CLIs, with explicit workspace or trusted-workstation launch modes, bounded command previews, multi-repository rules discovery, and an on-demand skills/recipes entry point. It lets agents review changes, run commands, and load only the smallest relevant rule or skill while reporting evidence and unverified steps.

An AI-native Taskwarrior bridge that provides natural language task management for Claude Code and other AI systems, integrating with existing Taskwarrior infrastructure.

It enables AI assistants to run installed command-line tools and scripts, drive persistent terminals and browsers, and retrieve saved jobs, screenshots, and other evidence on a server. It also provides a dashboard for tracking sessions, installed tools, and connection settings.

Discovery router for the agent-device CLI with status, install, help, prompts, and resources.

An MCP execution layer that lets capable AI models safely control a developer workstation for file edits, terminal and process execution, Git operations, language-server analysis, browser and Windows UI automation, media processing, job/resource control, and checkpointing. It also coordinates durable single- or multi-agent tasks across providers with leases, dependency graphs, evidence logs, repair loops, and separate verification gates.

A download tool for AI agents that wraps aria2 with safety rules and MCP server support, enabling reliable downloads without false positives.

Exposes tools over stdio that let an AI agent create disposable Docker sandboxes to run commands and stream output, read and write files, and take snapshots, roll them back, fork them, list, and destroy them. It tells the model to snapshot before risky changes so failed attempts can be undone or explored in parallel on self-hosted infrastructure.

Enables coding agents and users to share live tmux-based REPL sessions for many languages, allowing agents to execute code in the same interactive workspace via MCP.

Enables MCP clients to drive an installed Path of Building (PoE2) through the full pob command set as tools, allowing agents to read and modify builds, run calculations, search trade, and highlight locations in the PoB interface without modifying it.

Multi-threaded terminal management MCP server for AI assistants, enabling async command execution, batch operations, and real-time web monitoring with up to 100 concurrent terminals.

Local automation bridge that is both an MCP server and client: 234 tools across 44 namespaces for shell, filesystem, git, desktop control, browser (CDP), Android (ADB), OCR/ASR and memory, plus the ability to connect external MCP servers. Sandboxed execution with token auth, fail-closed policy and audit log.

Local daemon that bridges AI assistants to the local filesystem with read-only commands and optional write mode, enforcing strict security filters to prevent credential leakage.

MCP server for persistent SSH sessions with tmux integration, port forwarding, and SFTP file writes, built for AI agent workflows.