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

Enables comprehensive GitHub project management automation including creating/managing issues, updating project boards, assigning team members, and analyzing repository activity across organizations through OAuth-authenticated AI assistants.

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.

Perform Asana operations from an MCP Client such as Anthropic's Claude Desktop Application, and many more.

Enables Claude to interact with Asana workspaces, tasks and projects through the Asana API, allowing users to search, create, update, and manage Asana tasks and projects using natural language.

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.

Semantic navigator for MCP tools that finds relevant tools by natural language intent, reducing token usage by 95%.

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 assistants to manage project analysis, code metrics, documentation, Git operations, code quality, and file organization through natural language commands.

Shared team memory for AI coding agents with Bayesian confidence scoring and temporal decay, enabling persistent storage and retrieval of engineering patterns across sessions.

Enables grouping Conventional Commit subjects into Markdown release-note sections, including breaking changes, and scanning project files for TODO/FIXME/HACK/XXX comments with file:line while respecting project boundaries and result limits.

A Model Context Protocol server that provides a scratchpad tool for LLMs to organize thoughts, take notes, and plan approaches to complex problems.

Read, search, and write Obsidian vault notes via Team Relay collaborative server. Supports shared folders, real-time sync, and AI agent access.

MCP router that fronts your other MCP servers behind a few small generic tools, so their tool definitions stay out of the context window and are discovered on demand.

Project Memory stores decisions, evidence, outcomes and reviewed lessons in local SQLite. Assistants retrieve bounded context through three MCP tools. People follow and plan work in a live workspace with an overview, sprint board, decision history, readable documents, skills and project maps. Offline exports remain available.

Intent-based project documentation server that automatically maps natural language queries to the right sources (plans, todos, roadmap, docs) and provides comprehensive task management with dependency tracking, backlog promotion, and archival workflows.

A tool discovery MCP server that integrates with Azure DevOps wikis and code-graph-rag to match natural language goals to tools, enabling tool composition and proxy testing.

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.

MCP server for team task tracking, code review, and activity monitoring via Claude Code. Assign tasks, track branch activity, review code, and get daily summaries through natural language.

Enables coding agents to discover projects, inspect existing work, atomically claim tasks, record progress, and complete verified work through a task-tracking HTTP API, with version checks, claim coordination, and idempotent retries.

A FastMCP server that exposes predefined artifacts as Resources and project lifecycle operations as Tools for creating, updating, deploying, debugging, testing, monitoring, and configuring projects with minimal token usage.

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.

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

Publishes a shared MCP endpoint where API-key users in different groups see and call only the tools their policies permit, mixing remote upstream tools with local ones. Tool discovery and execution are both filtered, hidden-tool calls are denied before their handlers run, and every action is tagged with subject and group in structured JSON logs.

Provides a local-first memory layer for coding agents, storing principles, rules, and corrections on-device and surfacing them at the moments they apply so agents act on the intended outcomes without sending data to the cloud.

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 AI agents to query a repository's verified semantic model, including capabilities, ownership, contracts, constraints, impact, health, and verification status, without re-deriving project facts. It exposes read-only tools for inspecting, reasoning about, and verifying the provenance-tracked surface.

Manages Supabase projects, databases, edge functions, secrets, and branches across organizations using the Supabase Management API with 24 tools.

Enables AI assistants to programmatically manage GitHub Project V2 boards with 100+ tools for issues, fields, workflows, and automation via MCP.

Provides a unified MCP server to interact with multiple middleware (Loki, MySQL, Redis, RocketMQ, Elasticsearch, MongoDB) through a desktop application for connection management and configuration export.

Turns any OpenAPI spec into a single-tool MCP server where the model writes JavaScript against a typed api object in a QuickJS sandbox, so multi-step tasks run in one round trip and only the final result reaches context. Credentials, rate limiting, retries, write gating and audit logging stay on the host, outside the sandbox.