Taskade MCP Server connects Taskade to AI assistants like Claude and Cursor via the Model Context Protocol, enabling AI to manage workspaces, projects, and tasks. It offers 62 tools for comprehensive workspace, project, and AI agent management, allowing natural language interaction instead of direct API coding.

mcp: Taskade MCP Server connects Taskade to AI assistants like Claude and… (★ 153, MIT)

Features

01Connect Taskade to various AI assistants (Claude, Cursor, VS Code, etc.)

02Provides 62 tools for comprehensive workspace, project, task, and agent management

03Build, train, and deploy AI agents directly from your AI client

04Automate multi-step workflows across 100+ services via natural language

05Generate MCP tools from any OpenAPI 3.0+ specification

Why choose it

+Connect Taskade to various AI assistants (Claude, Cursor, VS Code, etc.)

+Project Management with AI: Manage projects, tasks, and workflows using natural language commands.

+Covers 7 supported environments or platforms, which is helpful for broader deployment needs.

+Ships with a public repository and a MIT license, which makes adoption and review easier.

Trade-offs

!There are at least 8 related tools in the same category, so the best choice is easier to make after side-by-side comparison.

Compatibility

Claude

AI Client

Verified via docs

Cursor

AI Client

Verified via docs

Windsurf

AI Client

Verified via docs

VS Code

IDE

Verified via docs

Cline

IDE Extension

Verified via docs

Node.js

Runtime

Verified via docs

Quick start

$ npx -y @taskade/mcp-server

Use cases

↳Project Management with AI: Manage projects, tasks, and workflows using natural language commands.

↳AI Agent Creation: Build, train, and publish autonomous AI agents with custom knowledge bases.

↳Template Workflows: Automate project creation and setup from existing templates.

↳Daily Standup Summarizer: Summarize completed tasks and generate priority tasks for daily standups.

↳Sprint Planning Automation: Automatically create sprint projects, move backlog items, and assign teams.

How it compares

≈mcp sits in the API Integration category, so it makes more sense to evaluate it alongside tools like awesome-mcp-servers instead of in isolation.

≈If your main need is closer to "Project Management with AI: Manage projects, tasks, and workflows using natural language commands.", that use case is a better lens for comparison than broad feature checklists alone.

≈mcp uses a MIT license, and community traction are both easier to judge in category context.