by teamwork
Official Teamwork.com server for project management tips, product updates, support, and collaboration — join Teamwork us
Official MCP server that connects AI tools to Teamwork.com project management platform. Enables AI agents to interact with tickets, customers, companies and other Teamwork data through a standardized protocol.
Teamwork is an official MCP server published by teamwork that provides AI assistants with tools and capabilities via the Model Context Protocol. Official Teamwork.com server for project management tips, product updates, support, and collaboration — join Teamwork us It is categorized under developer tools, productivity. This server exposes 108 tools that AI clients can invoke during conversations and coding sessions.
You can install Teamwork in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
MIT
Teamwork is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired Teamwork into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: Teamwork surfaces stars and publisher context so we could sanity-check maintenance before adopting.
According to our notes, Teamwork benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Teamwork is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Teamwork against two servers with overlapping tools; this profile had the clearer scope statement.
Useful MCP listing: Teamwork is the kind of server we cite when onboarding engineers to host + tool permissions.
Teamwork reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Teamwork has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Strong directory entry: Teamwork surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Teamwork is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
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Model Context Protocol server for Teamwork.com integration with Large Language Models
📌 Are you a Teamwork.com user wanting to connect AI tools (Claude Desktop, VS Code Copilot Chat, Gemini, etc.) to your Teamwork.com site right now? Jump straight to the Usage Guide (How to Connect) for tokens, enabling MCP and client configuration examples.
This MCP (Model Context Protocol) server enables seamless integration between Large Language Models and Teamwork.com. It provides a standardized interface for LLMs to interact with Teamwork.com projects, allowing AI agents to perform various project management operations.
Model Context Protocol (MCP) is an open protocol that standardizes how applications provide context to LLMs. This server describes all the actions available in Teamwork.com (tools) in a way that LLMs can understand and execute through AI agents.
This project provides three different ways to interact with the Teamwork.com MCP server:
Production-ready HTTP server for cloud deployments and multi-client support.
📖 Full HTTP Server Documentation
Quick start:
TW_MCP_SERVER_ADDRESS=:8080 go run cmd/mcp-http/main.go
Direct STDIO interface for desktop applications and development environments.
📖 Full STDIO Server Documentation
Quick start:
TW_MCP_BEARER_TOKEN=your-token go run cmd/mcp-stdio/main.go
Command-line tool for testing and debugging MCP server functionality.
Quick start:
go run cmd/mcp-http-cli/main.go -mcp-url=https://mcp.example.com list-tools
# Run all tests
go test ./...
# Run specific package tests
go test ./internal/twprojects/
For debugging purposes, use the MCP Inspector tool:
NODE_EXTRA_CA_CERTS=letsencrypt-stg-root-x1.pem npx @modelcontextprotocol/inspector node build/index.js
[!IMPORTANT] Note: The
NODE_EXTRA_CA_CERTSenvironment variable is required when using OAuth2 authentication with the Let's Encrypt certification authority. Download the certificate here.
├── cmd/
│ ├── mcp-http/ # HTTP server implementation
│ ├── mcp-stdio/ # STDIO server implementation
│ └── mcp-http-cli/ # CLI tool for testing via HTTP
├── internal/
│ ├── auth/ # Authentication helpers (bearer & OAuth2 token handling)
│ ├── config/ # Configuration management (env, flags)
│ ├── helpers/ # Shared utility functions (errors, link helpers, tool parsing)
│ ├── request/ # HTTP request primitives / Teamwork API wiring
│ ├── toolsets/ # Tool framework and registration logic
│ └── twprojects/ # Teamwork project/domain tools (tasks, tags, timers, etc.)
├── examples/ # Usage & integration examples (LangChain Node/Python)
├── usage.md # End-user setup & connection guide
├── Makefile # Common developer tasks
├── Dockerfile # Container build configuration
├── CODE_OF_CONDUCT.md # Community guidelines
├── CONTRIBUTING.md # Contribution guide
└── SECURITY.md # Security policy
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.