"Using agents in VSCode to reference library documentation during programming" matching MCP servers:

An MCP bridge that exposes A2A agents as tools in VS Code agent chat, enabling natural language delegation to remote agents with multi-turn conversation and configurable authentication.

Enables multiple AI agents using MCP-compatible clients to collaborate in parallel on the same project across one or more machines, with direct messaging, a shared task board, atomic file/folder locking, presence tracking, and a live web dashboard.

An MCP relay that adds a use_jev tool to every connected server, letting the Jev model autonomously select and execute tool calls while the calling model supplies any required text values. It enables agents to delegate multi-step tool use across all configured MCP servers through a single unified interface.

Enables AI assistants to delegate specific tasks to specialized sub-agents (e.g., test-writer, code-reviewer). Supports both Cursor and Claude Code with custom agent definitions.

Enables an AI agent to launch, monitor, and steer multiple coding agents running in tmux panes, including sending keystrokes, waiting for idle or output patterns, and managing sessions.

Enables local deterministic discovery, validation, and selection of validated skills through MCP tools, producing consistent plans without executing any skill.

Enables orchestrating multiple specialist Claude Code agents as a team, with live visualization and agent-to-agent messaging.

nexus-agents makes your AI coding tools work together intelligently. It coordinates Claude, Codex, Gemini, and OpenCode — routing each task to the best model using data-driven algorithms, validating outputs through multi-model consensus voting, and continuously improving through outcome-driven learning. Connect it to any MCP-compatible editor (Claude Code, Cursor, VS Code) and it handles the rest.

Enables running AI agents via OpenAI-compatible APIs with custom system prompts, models, and queries. Supports persistent memory, preset agents, and multi-step workflows like pipelines and swarms.

The simplest way to bridge and collaborate across AI Agent sessions like Claude Code, Codex, Gemini, or Cursor. It allows your agents to combine their strengths to solve your most difficult tasks without leaving their current context.

Enables running isolated main and sub agent tasks in networkless Docker capsules with explicit input grants, verified artifact delivery, and independent reviewer oversight via MCP tools.

MCP server that enables AI agents to autonomously buy virtual phone numbers and receive SMS verification codes. Supports multiple providers (5sim, SMS-Activate, OnlineSim) with automatic cheapest-provider selection.

Enhances Claude Code's Task tool with session resumption and independent agent execution, allowing autonomous subagents from .claude/agents/ with true parallel batch support.

Enables listing, inspecting, chatting with, and waking deployed Voight Agents from any MCP client, including checking agent state, usage, tasks, and GPU status.

Enables coding agents to publish and read events on channels, notify humans, and ask yes/no, multiple-choice, or free-text questions, returning answers as tool results.

Runs isolated AI agents in Docker containers with persistent workspaces and conversation history. Each agent has access to file operations, shell commands, GitHub integration, and can connect to external MCP servers for additional tools.

A lightweight framework for building and orchestrating AI agents through the Model Context Protocol, enabling users to create scalable multi-agent systems using only configuration files.

Enables one coding agent to inspect, configure, debug, and repair another agent over stdio MCP, with redacted reads and confirm-gated writes and restarts.

An MCP server that enables users to manage, search, and apply specialized AI personas within Claude Desktop and Claude Code CLI. It features a local-first architecture with YAML-based storage for organizing curated prompts and role collections.

Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.