"Asana: Project Management and Team Collaboration Tool" matching MCP servers:

Project Management AI - MCP server providing AI-powered tools and automation by MEOK AI Labs

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.

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

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 Claude to collaborate with Gemini for code reviews, second opinions, and iterative software development. It facilitates multi-step workflows including PRD creation and code generation through an AI orchestration framework.

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 coding agents in different editors and MCP clients to share messages, project context, notes, task ownership, handoffs, and advisory file claims through a local SQLite-backed coordination server. Supports MCP stdio, MCP Streamable HTTP, a JSON HTTP API, and a local CLI for multi-project agent collaboration.

Enables collaboration with multiple AI providers (Claude, GPT-4, Gemini, Ollama) directly from VS Code with automatic project context injection and persistent conversation history. Provides streamlined tools for getting AI advice, multi-provider research, and enhanced context sharing across sessions.

Enables AI agents to interact with collaborative Jupyter notebooks and documents in real-time, supporting notebook operations, document editing, user awareness tracking, and session management through Jupyter's RTC capabilities.

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

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.

Facilitates AI session handoffs and next steps tracking through project-based organization, supporting task prioritization and seamless workflow management.

A server built on mcp-framework that enables integration with Claude Desktop through the Model Context Protocol.

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.

Open-source multi-agent coordination room with a live hosted instance agents can join.

Provides a 'reflect' tool that creates cognitive checkpoints for AI assistants, forcing structured step-by-step reasoning through complex problems to improve accuracy and maintain context during task execution.

Provides specialized tools for portfolio health analysis, rebalance simulations, and trade execution via a single interface powered by MongoDB. It enables AI agents to manage investment portfolios while adhering to organizational governing rules, clearance guards, and rate limits.

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

Autonomous AI software development pipeline that transforms tickets into production-ready code through planning, coding, testing, reviewing, and delivery stages.

Provides a no-op 'think' tool that enables Claude to decompose complex problems and enhance reasoning by pausing to organize thoughts.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Implements Anthropic's 'think' tool for Claude, providing a dedicated space for structured reasoning during complex problem-solving tasks that improves performance in reasoning chains and policy adherence.

MCP server that enables AI assistants to run multi-step agent pipelines (e.g., Issue Analyst → Code Writer → Test Runner → PR Opener) from conversations, with support for Devin, shell, Python, and HTTP agents.

Enables AI project feasibility evaluation and solution planning through structured reasoning stages, using Gemini API and web search for up-to-date research.

An MCP server implementation of Anthropic's Think Tool prompt engineering technique that enables Claude to break down complex problems and enhance its reasoning capabilities by providing a simple tool that echoes back thoughts.

A Model Context Protocol server that provides Claude with a dedicated space for structured thinking during complex problem-solving tasks, helping improve its reasoning capabilities.

An MCP server that transforms text into knowledge graphs and autonomously generates insights by combining Montague Grammar with Zettelkasten methodology.

Maximizes AI agent context window by enabling compact code reading and editing, reducing tokens by 40% for deeper codebase understanding.

This is a powerful Model Context Protocol (MCP) server that integrates multiple AI coding agents—Anthropic Claude Code, OpenAI Codex, and Google Gemini—directly into your workflow. It enables seamless cross-provider analysis, leveraging Gemini's massive token window, Codex's specialized coding capabilities, and Claude's advanced reasoning.

Enables autonomous AI-to-AI collaboration between Claude and Gemini to execute complex development projects with minimal human intervention. It provides a role-based system with task dependencies, automated project planning, and continuous execution loops.