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DHTMLX To Do List MCP server: tasks, projects, and REST sync

DHTMLX To Do List looks like a simple checklist on the surface, but it packs in nested subtasks, priority hotkeys, drag-and-drop reordering, hashtag-based filtering, and a REST backend that chains through the event bus. If generated code is going to work, it needs the current signatures for copying a task or indenting one into a subtask, the right event payload shape, and the task field options that actually exist today, not a stale training memory.

That's where the DHTMLX MCP server helps: it lets the assistant check the live To Do List documentation before it answers. Point it at task operations, multiselection and bulk actions, the REST backend integration, or task and project configuration, and the assistant retrieves the current API surface instead of guessing.

MCP endpoint​

https://docs.dhtmlx.com/mcp
note

The DHTMLX MCP server covers all major DHTMLX products, not only DHTMLX To Do List. The same endpoint and configuration instructions work regardless of which DHTMLX component you are building with.

MCP server support across the To Do List API​

The server's index covers the full DHTMLX To Do List documentation, including:

How a To Do List question moves through the MCP server​

Ask the DHTMLX MCP server anything about To Do List, and the request runs through a Retrieval-Augmented Generation (RAG) pipeline over the Model Context Protocol (MCP). One of two workflows picks it up from there: Search hands back matching reference pages for the assistant to write from, while Inference reads those same pages and answers directly. A To Do List request often bundles two separate jobs: one that needs current documentation, and one the assistant can already do on its own. It peels off the first before anything reaches MCP.

Here's that process for the prompt "How do I make sure add-task and move-task operations reach my server in the right order?":

  1. The assistant flags the documentation job: how to chain RestDataProvider into the event bus with api.setNext().
  2. The server pinpoints the working-with-server documentation it belongs to.
  3. Generating this handler calls for code, so the request goes to Search (a narrower question, like which event fires first in the chain, would go to Inference instead).
  4. Search fetches the matching pages from a vector index built on the current To Do List documentation.
  5. Those pages land back with the assistant as context.
  6. The assistant assembles the event bus chaining code from that context, then fills in the server-specific request details from its own knowledge instead of guessing at the To Do List API.

Because of that lookup, the assistant wires RestDataProvider into the event bus the way it actually works today.

Setting up the MCP connection​

Setting up a fresh To Do List project or hooking into an existing RestDataProvider backend both start the same way: point your tool at the URL below, via a CLI command or a JSON configuration entry, depending on what that tool expects.

https://docs.dhtmlx.com/mcp

The following sections cover setup, one tool at a time.

Claude Code​

info

For the complete rundown of Claude Code's MCP setup, check the official documentation.

To register the server from the command line, run:

claude mcp add --transport http dhtmlx-mcp https://docs.dhtmlx.com/mcp

Manual setup works too, just add this to your .mcp.json:

{
  "mcpServers": {
    "dhtmlx-mcp": {
      "type": "http",
      "url": "https://docs.dhtmlx.com/mcp"
    }
  }
}

Cursor​

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Step-by-step MCP configuration for Cursor lives in the official documentation.

Steps to add the server:

  1. Open Settings (Cmd+Shift+J on Mac, Ctrl+Shift+J on Windows/Linux)
  2. Go to Tools & MCP
  3. Click Add Custom MCP
  4. Paste the following config:
{
  "mcpServers": {
    "dhtmlx-mcp": {
      "url": "https://docs.dhtmlx.com/mcp"
    }
  }
}

Google Antigravity​

Antigravity 2.0​

info

Antigravity documents its MCP server integration in the official documentation.

These are the steps to complete for connecting DHTMLX MCP server with Google Antigravity:

  1. Open the command palette
  2. Type "mcp add"
  3. Select "HTTP"
  4. Provide the following values:
  • Name:
dhtmlx-mcp
  • URL:
https://docs.dhtmlx.com/mcp

Antigravity CLI​

info

For Gemini CLI to Antigravity CLI migration steps, check the related guide.

To connect the DHTMLX MCP server to Antigravity CLI, create mcp_config.json in one of these locations:

  • Global: ~/.gemini/config/mcp_config.json
  • Workspace: .agents/mcp_config.json

Add the following configuration:

{
  "mcpServers": {
    "dhtmlx-mcp": {
      "serverUrl": "https://docs.dhtmlx.com/mcp"
    }
  }
}

Then run agy in the terminal.

ChatGPT​

info

ChatGPT's own documentation lays out the complete MCP connector setup process.

Steps to configure the connector:

  1. Go to Settings → Apps & Connectors
  2. Click Advanced settings
  3. Enable Developer mode
  4. Return to Apps & Connectors and click "Create"
  5. Fill in the connector details:
  • Name:
dhtmlx-mcp
  • URL:
https://docs.dhtmlx.com/mcp
  • Authentication: No authentication
  1. Click Create

After you create the connector, ChatGPT pulls documentation from the MCP server during conversations.

info

For intensive coding workflows, other MCP-aware tools may be a better fit.

Other tools​

Most other AI coding tools support MCP too, typically under a settings label like "Model Context Protocol" or "Context Sources." Add https://docs.dhtmlx.com/mcp there as a custom source.

How the MCP server treats your data​

The MCP server is a hosted service: nothing runs on your machine, no files from your environment are read, and no personal user data is stored.

For debugging and service improvement, queries sent to the server may be logged.

If your organization needs stricter guarantees, ask about a commercial deployment with query logging turned off by writing to info@dhtmlx.com.

To Do List prompts to copy and adapt​

Each group below targets one kind of operation. Copy an example and adapt the wording to your own data.

Tasks and hierarchy

How do I add a subtask under a specific task in DHTMLX To Do List?
How do I make a task become a subtask of the task directly above it? Use the docs.
How do I copy a task along with its subtasks and paste it into a different project?

Projects

How do I switch to a different active project and move a task into it?
What happens to a project's tasks when I delete the project in DHTMLX To Do List?

Selection and bulk operations

How do I select several tasks and mark them all as complete at once in DHTMLX To Do List?
How do I delete all currently selected tasks at once? Use the docs.

Server integration

How do I connect DHTMLX To Do List to my Node.js backend and load tasks on initialization?
How do I set up multiuser mode so task changes sync across clients in real time?

Making To Do List prompts more precise​

  • Name the exact method. DHTMLX To Do List exposes several similarly named method pairs (checkTask()/uncheckTask(), indentTask()/unindentTask(), hideCompletedTasks()/showCompletedTasks()). State the method name so the assistant retrieves the right reference instead of guessing the parameters.
  • Say whether the target is a task, a project, or a user. Many methods take a similar id-based object (for example, getTask() vs. getProject()). Naming the target narrows retrieval to the correct part of the API.
  • Describe the data shape. Prompts like "a task with subtasks and a due date" or "a task assigned to several users" retrieve more precise documentation than a generic "a task". This matters most when preparing data to load or configuring taskShape.
  • Add "Use the docs" to your prompt. This phrase signals that the assistant should trigger an MCP lookup instead of answering from training data alone. It helps most with event payloads (for example, open-menu or edit-item) and REST backend wiring, both of which change between versions.
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