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Teamwork

teamwork

by teamwork

Official Teamwork.com server for project management tips, product updates, support, and collaboration — join Teamwork us

Official server for Teamwork.com project management platform

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Official Teamwork.com integrationMultiple transport modes (HTTP/STDIO)Optional read-only mode for safety

best for

  • / Project managers using AI to streamline Teamwork workflows
  • / Support teams automating ticket management
  • / Organizations integrating AI assistants with existing Teamwork setups

capabilities

  • / Create and manage support tickets
  • / Create companies and customers in Teamwork Desk
  • / Upload files and attachments
  • / Send reply messages to tickets
  • / Create custom priorities, statuses, tags and ticket types
  • / Retrieve company information and records

what it does

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.

about

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.

how to install

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.

license

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.

readme

Teamwork MCP Server

Model Context Protocol server for Teamwork.com integration with Large Language Models

Go Report Card Go MCP

📌 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.

📖 Overview

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.

🤖 What is MCP?

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.

✨ Features

  • Multiple Transport Modes: HTTP and STDIO interfaces for different deployment scenarios
  • Secure Authentication: Bearer token and OAuth2 integration with Teamwork.com
  • Tool Framework: Extensible toolset architecture for adding new capabilities
  • Production Ready: Comprehensive logging, monitoring, and observability
  • Read-Only Mode: Optional restriction to read-only operations for safety

🚀 Available Servers

This project provides three different ways to interact with the Teamwork.com MCP server:

📡 HTTP 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

💬 STDIO Server

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

🛠️ HTTP CLI

Command-line tool for testing and debugging MCP server functionality.

📖 Full HTTP CLI Documentation

Quick start:

go run cmd/mcp-http-cli/main.go -mcp-url=https://mcp.example.com list-tools

📋 Prerequisites

  • Go 1.26 or later
  • Valid Teamwork.com API credentials (bearer token or OAuth2 setup)

🧪 Development & Testing

Running Tests

# Run all tests
go test ./...

# Run specific package tests
go test ./internal/twprojects/

MCP Inspector

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_CERTS environment variable is required when using OAuth2 authentication with the Let's Encrypt certification authority. Download the certificate here.

🏗️ Architecture

├── 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

FAQ

What is the Teamwork MCP server?
Teamwork is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Teamwork?
This profile displays 53 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.6 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

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

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

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

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ 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.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.653 reviews
  • Ganesh Mohane· Dec 28, 2024

    We wired Teamwork into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Ishan Khanna· Dec 16, 2024

    Strong directory entry: Teamwork surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Diego Martin· Dec 12, 2024

    According to our notes, Teamwork benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Aarav Liu· Dec 8, 2024

    Teamwork is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • Aarav Farah· Nov 27, 2024

    We evaluated Teamwork against two servers with overlapping tools; this profile had the clearer scope statement.

  • Fatima Srinivasan· Nov 27, 2024

    Useful MCP listing: Teamwork is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Rahul Santra· Nov 19, 2024

    Teamwork reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Diego Farah· Nov 3, 2024

    Teamwork has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Henry Khan· Nov 3, 2024

    Strong directory entry: Teamwork surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Camila Thomas· Oct 22, 2024

    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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