ActiveCollab MPC Server

MCP (Model Context Protocol)

This feature is currently available to users in the Early Access Program.

What is MCP and how does it work in ActiveCollab?

MCP (Model Context Protocol) allows AI assistants to securely connect to your ActiveCollab workspace and interact with your data.

When MCP is enabled, supported AI models can access information from your workspace and help you work faster. Depending on the permissions you allow, the AI can:

  • Look up project and task details

  • Answer questions about your projects

  • Summarize discussions

  • Find information across tasks and comments

  • Create or update tasks (if write access is enabled)

For example, you could ask an AI assistant:

  • “Show me all overdue tasks in the Website Redesign project.”

  • “Summarize the discussion in task #4521.”

  • “Create a task for preparing the Q2 marketing report.”

The AI interacts with ActiveCollab through a secure token generated in Apps & Integrations.

Only the Owner of the ActiveCollab workspace can create and manage MCP tokens.

Once a token is created, it can be used to connect an AI client such as:

  • Langdock

  • Claude (Anthropic)

  • Other MCP-compatible clients

After the connection is established, the AI can safely access the parts of ActiveCollab allowed by the token.

How to enable MCP in ActiveCollab?

  1. Go to Apps & Integrations.
  2. Open the MCP section.
  3. Click Add MCP Client.
  4. Enter your account password to confirm the action.
  5. Choose the AI client you want to connect:
    1. Langdock
    2. Claude
    3. Other
  6. Configure the connection and generate the token.
ActiveCollab will generate an MCP server URL and access token that your AI client will use to connect.

How to connect Langdock to ActiveCollab

If you are using Langdock, you can connect it to ActiveCollab by creating an MCP token and adding it as a remote MCP integration in Langdock.


  1. Go to Apps & Integrations → MCP.
  2. Click Add MCP Client.
  3. Enter your password.
  4. Select Langdock.
  5. (Optional) Enable Token is read-only if you want the AI to only view data.
  6. Click Add.

ActiveCollab will generate:

  • MCP Server URL
  • MCP Token

How to connect Claude to ActiveCollab?

If you are using Claude Desktop, you can connect it to ActiveCollab using the MCP configuration snippet generated in ActiveCollab.


  1. Go to Apps & Integrations → MCP.
  2. Click Add MCP Client.
  3. Enter your password.
  4. Select Claude.
  5. Click Add.

ActiveCollab will generate:

  • MCP Server URL
  • MCP Token
  • A configuration snippet for Claude Desktop

How to connect another MCP-compatible AI client?

If you want to connect another AI tool that supports MCP, you can generate a custom token and configure it in that client.


If you are using another MCP-compatible client:

  1. Go to Apps & Integrations → MCP.
  2. Click Add MCP Client.
  3. Enter your password.
  4. Select Other.
  5. Enter:
    1. Client – the name of the application using the token
    2. Vendor – the organization that built the application
  6. Choose the access level:
    1. Read Only – the AI can only view data
    2. Read and Write – the AI can create and update items
  7. Click Add.

ActiveCollab will generate:

  • MCP Server URL
  • MCP Token
Use these credentials in your AI client when configuring the MCP connection.

Who can create MCP tokens?

Only the Owner of the ActiveCollab workspace can create MCP tokens.

Tokens are generated from the Apps & Integrations → MCP page.

If multiple team members need access to the AI client, the Owner can share the client credentials with the team.

However, we recommend using a dedicated business account for the AI client instead of a personal account. This prevents sharing personal login credentials and makes access management easier.

Can multiple AI models use MCP at the same time?

Yes, you can use the same MCP server URL and token with multiple AI clients.

However, we recommend creating separate tokens for each client. This gives you better visibility and control over how each AI integration is used.

Creating separate tokens also makes it easier to:

  • track usage
  • manage permissions
  • revoke access for a specific client if needed.

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