About Teamwork.com AI
An unofficial Teamwork.com extension with an assigner service that processes task webhooks. It extracts skills and job roles from tasks to assign a suitable user.
Key features
- 01Processes task creation and update webhooks
- 02Extracts skills and job roles from tasks
- 03Assigns a user to each task
Use cases
- 01Assigning task owners from task details
- 02Handling Teamwork.com task updates
MCP support
The MCP server has moved to the official Teamwork.com repository.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMETeamwork.com AI
Unofficial extension for Teamwork.com to integrate AI capabilities.
Warning
When interacting with LLMs, be aware that the data you provide may be used to train and improve AI models. This may include sharing your data with third-party providers, which could lead to potential privacy and security risks. Always review the terms of service and privacy policies of the AI providers you use to understand how your data will be handled.
MCP server
The MCP server was moved to the official Teamwork.com repository: https://github.com/teamwork/mcp
Assigner
The assigner is a webservice that integrates with Teamwork.com webhooks, handling task creation and updates. It behave as an Agentic AI, extracting skills and job roles from the task and assigning the best user to fulfill it.
For more information check our documentation.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.