What runtime does Taskqueue require?
Taskqueue requires Node.js to run. It is executed via `npx`, which downloads and runs the `taskqueue-mcp` package.
@chriscarrollsmith
Basic information
No overview available yet
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"taskqueue": {
"command": "npx",
"args": [
"-y",
"taskqueue-mcp"
]
}
}
}List all projects in the system and their basic information (ID, initial prompt, task counts), optionally filtered by state (open, pending_approval, completed, all).
Read all information for a given project, by its ID, including its tasks' statuses.
Create a new project with an initial prompt and a list of tasks. This is typically the first step in any workflow.
Delete a project and all its associated tasks.
Add new tasks to an existing project.
Mark a project as complete. Can only be called when all tasks are both done and approved. This is typically the last step in a project workflow.
Use an LLM to generate a project plan and tasks from a prompt. The LLM will analyze the prompt and any attached files to create a structured project plan.
List all tasks, optionally filtered by project ID and/or state (open, pending_approval, completed, all). Tasks may include tool and rule recommendations to guide their completion.
Get details of a specific task by its ID. The task may include toolRecommendations and ruleRecommendations fields that should be used to guide task completion.
Create a new task within an existing project. You can optionally include tool and rule recommendations to guide task completion.
Modify a task's properties. Note: (1) completedDetails are required when setting status to 'done', (2) approved tasks cannot be modified, (3) status must follow valid transitions: not started → in progress → done. You can also update tool and rule recommendations to guide task completion.
Remove a task from a project.
Approve a completed task. Tasks must be marked as 'done' with completedDetails before approval. Note: This is a CLI-only operation that requires human intervention.
Get the next task to be done in a project. Returns the first non-approved task in sequence, regardless of status. The task may include toolRecommendations and ruleRecommendations fields that should be used to guide task completion.
Taskqueue is a Model Context Protocol (MCP) server for AI task management. It helps AI assistants handle multi-step tasks in a structured way, with user approval checkpoints. It is designed for use with MCP clients like Claude Desktop or Cursor.
Configure the tool in your MCP client using the command npx -y taskqueue-mcp. Optionally set the TASK_MANAGER_FILE_PATH environment variable to specify a custom storage location. Use the CLI utility (npx task-manager-cli --help) for task approval and listing.
Taskqueue requires Node.js to run. It is executed via `npx`, which downloads and runs the `taskqueue-mcp` package.
Data is stored in a JSON file in a platform-specific default location: `~/.local/share/taskqueue-mcp/tasks.json` (Linux), `~/Library/Application Support/taskqueue-mcp/tasks.json` (macOS), or `%APPDATA%\taskqueue-mcp\tasks.json` (Windows). A custom path can be set via the `TASK_MANAGER_FILE_PATH` environment variable.
Tasks follow a workflow: `not started` → `in progress` → `done`. You can move back from `in progress` to `not started`, and from `done` to `in progress` if additional work is needed. Approved tasks cannot be modified.
Task approval is controlled exclusively by the human user through the CLI command `npm run approve-task -- <projectId> <taskId>`. Tasks must be marked as "done" with completed details before they can be approved (unless using the `--force` option).
The README does not specify authentication or transport details. It uses the standard MCP protocol over stdio (implied by the client configuration). No authentication mechanism is mentioned.
Open-source MCP to use Blender with any LLM

Model Context Protocol Servers

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