Enterprise Task Queue Mcp Server
@wizd
About Enterprise Task Queue Mcp Server
TaskQueue MCP是一个革命性的企业级Agent执行引擎，核心采用BullMQ作为任务队列基础设施，实现了突破性的双循环分离架构。通过解耦用户对话循环与代理执行循环，该引擎能够执行持续数小时甚至数天的复杂工作流，同时保持完整的执行透明度与控制力。
Basic information
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"taskqueue-local": {
"command": "npx",
"args": [
"-y",
"taskqueue-mcp"
]
},
"taskqueue-public": {
"url": "https://taskqueue-public.vcorp.ai/rest/your-unique-chat-id",
"stream": true
}
}
}Tools
14List 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.
Overview
What is Enterprise Task Queue Mcp Server?
Enterprise Task Queue Mcp Server (npm package: taskqueue-mcp) is a Model Context Protocol (MCP) server for AI task management. It helps AI assistants handle multi-step tasks in a structured way, with optional user approval checkpoints.
How to use Enterprise Task Queue Mcp Server?
Configure it in an MCP client (e.g., Claude Desktop, Cursor) with the command npx -y taskqueue-mcp. Optionally set environment variables for LLM provider API keys. Use the CLI via npx taskqueue --help or npx --package=taskqueue-mcp taskqueue --help for human interaction like task approval.
Key features of Enterprise Task Queue Mcp Server
- Task planning with multiple steps
- Progress tracking
- User approval of completed tasks
- Project completion approval
- Task details visualization
- Task status state management
- Enhanced CLI for task inspection and management
Use cases of Enterprise Task Queue Mcp Server
- AI assistant managing a multi-step software development project.
- Structured workflow with human-in-the-loop approval for each task.
- Breaking down a complex user request into ordered, trackable subtasks.
- Coordinating task completion and final project sign-off.
FAQ from Enterprise Task Queue Mcp Server
What data does the server store and where?
It stores projects and tasks in a local JSON file. The default location is platform-specific: ~/.local/share/taskqueue-mcp/tasks.json on Linux, ~/Library/Application Support/taskqueue-mcp/tasks.json on macOS, and %APPDATA%\taskqueue-mcp\tasks.json on Windows.
How do I require manual human approval for tasks?
Set autoApprove to false when creating a project. Then the human user must use the CLI command npx taskqueue approve-task -- <projectId> <taskId> to approve tasks.
What are the environment variables for and which are required?
Optional API keys for generating project plans using LLM providers: OPENAI_API_KEY (OpenAI), GOOGLE_GENERATIVE_AI_API_KEY (Gemini), and DEEPSEEK_API_KEY (Deepseek). They are not required for basic task management.
Can I use a custom file path for the task data?
Yes, set the TASK_MANAGER_FILE_PATH environment variable in both the MCP client configuration and your shell before running the CLI. The same path must be used everywhere.
What are the task status transition rules?
Tasks follow not started → in progress → done. From in progress they can go back to not started. From done they can go back to in progress. Approved tasks cannot be modified, and a project can only be finalized when all tasks are both done and approved.
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