"Collaboration Between Multiple AIs via MCP (Master Control Program)" matching MCP servers:

Enables routing context and execution across AI tools like Claude, Cursor, Windsurf, and ChatGPT with a shared memory, task board, and context bus, plus local file conversion.

In-memory MCP server for cooperative collaboration between separate agents, enabling topic-based messaging and coordination via tools like create_topic, join_topic, send_message, read_messages, and check_in.

A multi-agent collaborative development bridge that enables TRAE Work to offload complex tasks to Codex executor, with tools for task submission, status tracking, artifact listing, and permission management.

Enables multi-agent code review and iterative improvement loop using different LLM backends, with deterministic scoring and audit trail.

A local MCP server that controls two Codex CLI agent instances via tmux, enabling parallel agent operations with lifecycle management, status classification, and secure path handling.

Enables AI agents from different providers to collaborate in shared discussion threads, posting proposals and reviews while retrieving synchronized context, with human oversight.

Aggregates multiple MCP servers behind a single, secure endpoint with unified tool/resource discovery, OAuth authentication, and resilient request routing. Enables users to manage and interact with multiple MCP backends through one centralized interface with load balancing and circuit breakers.

Centralized MCP server that intelligently routes user queries to appropriate MCP tools, aggregates responses from multiple servers, and supports both rule-based and AI-powered routing.

Lets any number of concurrent Claude Code sessions drive a Firefox browser through the Browser Control extension by funneling every session's commands over a single shared port via a background daemon and private unix socket. Sessions no longer have to compete for a limited pool of extension ports, and requests stay isolated through per-request correlation ids.

Enables AI agents to drive a real headful Chrome browser using vision-first computer interactions, with support for multi-agent tab groups, password-manager-filled logins, and human handoff.

Enables LLMs to orchestrate AI agents and tasks, inspect DAG workflows, monitor cluster metrics, and perform safe database queries and statements through standardized MCP tools.

Enables users to deploy a production-grade control plane for agents, providing a spec-compliant MCP server, scoped tool grants, failure-mode detection, and cost governance.

A central knowledge hub that connects AI tools to a unified 'Master Brain' with automated memory capabilities. It utilizes intelligent analysis to automatically capture and store key decisions, fixes, and learnings across various development environments.

A native MCP server that gives AI assistants full Chrome control via the DevTools Protocol and an unpacked extension, including semantic element refs, real input, screenshots, and browser/tab/window management. It exposes 43 callable tools with a visible control overlay and an optional read-only security profile.

An MCP server paired with a Firefox extension that enables LLM clients to control the user's browser, supporting tab management, history search, and content reading.

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

Enables AI agents to run durable, isolated, and observable browser automation tasks via Playwright, with persistent profiles, recovery, evidence capture, and unattended execution for long-running web workflows.

Enables AI agents to control existing Chrome and Firefox browser sessions locally, including tab management, navigation, form filling, screenshots, and persistent scripts, without remote relays or debugging ports.

Reliable Codex Desktop automation for long tasks with durable async operations, Plan Mode workflows, and retry-safe task submission.

Enables centrally authoring and validating reusable agent guidance and delivering it across MCP-compatible hosts via registry resources, prompt templates, skill execution, and shared state.