"Steam" matching MCP servers:

An event-driven MCP server that enables agents to share context streams, publish and subscribe to events, manage tasks, and follow protocols, keeping a fleet of agents mutually context-aware in real time.

Connects AI assistants to the ADAS platform, enabling them to build, validate, and deploy multi-agent systems through natural language commands without manual configuration.

Enables orchestrating a reusable team of expert agents (PM, architecture, development, QA, security) to analyze a project's files and produce consolidated recommendations. It exposes tools to list experts, ask a single expert, or run the full team from MCP clients.

Enables users to leverage a team of specialized AI agent personas like Business Analysts and Product Managers through a unified interface across multiple LLM providers. It streamlines agile development workflows by providing tools for model discovery, unified prompt delivery, and role-specific decision making.

Turns Claude Code into a self-driving AI company with autonomous CEO, agent teams, and self-improvement capabilities.

Enables collaboration between multiple AI models (GPT, Claude, Gemini) to work together on complex tasks, with intelligent task distribution and role-based expert assignment for code development, review, and optimization.

Enables delegating coding tasks to the Pi CLI through MCP with named sessions, synchronous or asynchronous execution, and a read-only transcript window on Windows.

A Model Context Protocol server that allows AI applications to interact with Crawlab's functionality through natural language, enabling spider management, task execution, and file operations.

Enables autonomous hierarchical multi-agent orchestration for MCP, with persistent SQLite state, real-time web monitoring, and support for mixing multiple CLI and direct API execution providers.

A Model Context Protocol (MCP) server that turns multiple AI coding agents into a coordinated team that chats, debates, remembers, audits security, and works in parallel on the same project.

This MCP server provides research-backed prompt optimization tools and professional domain templates designed to improve AI performance through strategies like Tree of Thoughts and Medprompt. It enables users to analyze, auto-optimize, and refine prompts using advanced reasoning patterns and safety-critical alignment techniques.

Enables Claude Code to orchestrate a team of independent Claude Code sessions within iTerm2 for parallel task execution and isolated git worktree management. It provides tools to spawn, monitor, and message worker sessions while maintaining full visibility and control over their terminal windows.

A reusable AI software development team built on MCP. 13 specialized agents (Project Manager, Backend, Frontend, QA, Security, DevOps, UX, and more) collaborate via shared SQLite state. Exposes 44 MCP tools across 12 domains. All discussions and decisions are stored in the database so agents get back up to speed immediately when re-loaded.

A Model Context Protocol server that provides safe, intelligent read/write access to NetBox instances, designed with safety-first principles for Large Language Model automation.

Enables real-time communication and orchestration of multiple AI agents with a web dashboard for monitoring agent activities, tasks, and artifacts.

A multi-agent collaboration platform that provides access to over 1,500 models from 68 providers via the Model Context Protocol. It enables users to assemble and coordinate specialized AI teams using advanced orchestration modes like swarm, debate, and hierarchical workflows.

A Model Context Protocol server for orchestrating red team security assessments, enabling LLMs to manage agents, targets, operations, and findings aligned with MITRE ATT&CK framework.

A Next.js-based MCP server template that implements OAuth 2.1 authentication with support for both SSE and Streamable HTTP transports. Provides a reference implementation for building authenticated MCP servers that work with Claude, Cursor, VSCode, and other MCP clients.

Enables users to plan, coordinate, and govern multi-agent teams using MongoDB for shared context, token budgets, and checkpoint recovery.

Enables structured role-to-role handoffs and merge gating for multi-agent collaboration. It persists evidence and computes approval gates without invoking LLMs.