Meta Mind MCP Server - Advanced Model Context Protocol server for intelligent task management, workflow orchestration, and automatic archiving with hierarchical structures and agent specialization
npm install @snapspecter/mcp-meta-mindA sophisticated Model Context Protocol (MCP) server that implements intelligent task management and workflow orchestration with hierarchical task structures, automatic archiving, and comprehensive progress tracking.
Meta Mind MCP Server is a technical implementation of the Model Context Protocol that provides advanced task management capabilities for AI agents. It serves as a centralized task orchestration system that can be integrated with various MCP clients including Claude Desktop, KiloCode, RooCode, and other compatible systems.
The server provides a comprehensive suite of tools for:
- Task Planning & Organization: Creates and manages hierarchical task structures with complex dependencies
- Workflow Orchestration: Coordinates task execution across multiple concurrent projects
- Progress Tracking: Monitors task completion, generates analytics, and provides real-time status updates
- Artifact Management: Logs and tracks generated files, code, documentation, and other outputs
- Automatic Archiving: Intelligently archives completed task trees to maintain clean active workspaces
- Summary Generation: Creates detailed markdown summaries of completed work with reasoning and artifacts
| Tool | Purpose |
|------|---------|
| request_planning | Create new project requests with task breakdowns |
| get_next_task | Intelligent next task selection based on priorities and dependencies |
| mark_task_done | Complete tasks with artifact logging and automatic archiving |
| mark_task_failed | Handle task failures with retry strategies |
| open_task_details | Deep dive into specific task information |
| list_requests | Overview of all active projects and their status |
| add_tasks_to_request | Dynamically add tasks to existing projects |
| update_task | Modify task properties, priorities, and metadata |
| add_dependency / remove_dependency | Manage task relationships |
| validate_dependencies | Ensure dependency graphs are valid |
| delete_task | Remove tasks and their descendants |
| add_subtask / remove_subtask | Manage hierarchical task structures |
| archive_task_tree | Manual archiving of completed task trees |
| log_task_completion_summary | Generate detailed markdown summaries |
| split_task | Break down complex tasks into manageable subtasks |
| merge_tasks | Combine related tasks for better organization |
bash
npm install -g @snapspecter/mcp-meta-mind
`$3
`bash
mkdir -p ~/.meta_mind/mcp_task_manager_data
`Configuration
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#### Global Installation (Recommended)
`json
{
"mcpServers": {
"meta-mind": {
"command": "npx",
"args": ["-y", "@snapspecter/mcp-meta-mind"]
}
}
}
`#### Direct Executable Path
`json
{
"mcpServers": {
"meta-mind": {
"command": "/path/to/mcp-meta-mind/dist/index.js"
}
}
}
`#### Development Setup (Local Build)
`json
{
"mcpServers": {
"meta-mind-dev": {
"command": "node",
"args": ["dist/index.js"],
"cwd": "/absolute/path/to/mcp-meta-mind"
}
}
}
`#### Development Setup (TypeScript)
`json
{
"mcpServers": {
"meta-mind-dev": {
"command": "tsx",
"args": ["./index.ts"],
"cwd": "/absolute/path/to/mcp-meta-mind"
}
}
}
`Technical Architecture
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- SQLite backend with automatic schema initialization
- Tasks table storing hierarchical task data with relationships
- Requests table managing project-level information
- Artifacts table tracking generated files and outputs$3
`
~/.meta_mind/
├── tasks.db # SQLite database
└── completed_task_summaries/ # Generated task summary files
`$3
- pending: Ready to be worked on
- active: Currently being executed
- done: Successfully completed
- failed: Failed with retry options
- requires-clarification: Needs additional informationDevelopment
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`bash
Clone repository
git clone https://github.com/snapspecter/mcp-meta-mind.git
cd mcp-meta-mindInstall dependencies
npm installBuild project
npm run buildStart development server
npm run start
`$3
`bash
npm run build
``#### Multi-Modal Reasoning
- Sequential Thinking: Step-by-step logical progression through complex problems
- Chain of Thought (CoT): Detailed reasoning chains with intermediate steps and validation
- Chain of Density (CoD): Iterative refinement of solutions with increasing detail and accuracy
#### Reasoning Transparency & Audit Trail
AI agents will have complete reasoning transparency with comprehensive logging systems that capture:
- Decision Point Analysis: Why specific approaches were chosen over alternatives
- Problem Decomposition Logic: How complex tasks were broken down into manageable components
- Dependency Resolution Reasoning: The logic behind task ordering and dependency management
- Priority Assessment Rationale: Detailed explanations for task prioritization decisions
- Failure Analysis: Root cause analysis and learning from failed attempts
This reasoning audit trail enables:
- Debugging AI Decision Making: Understand exactly why an agent made specific choices
- Performance Optimization: Identify patterns in successful vs. unsuccessful reasoning approaches
- Knowledge Transfer: Reuse successful reasoning patterns across similar problems
- Continuous Improvement: Refine agent behavior based on reasoning outcome analysis
#### Web-Based Management Interface
A lightweight web server will provide comprehensive task management capabilities:
Dashboard Features:
- Interactive Task Browser: Navigate hierarchical task structures with expandable trees
- Real-Time Progress Visualization: Dynamic progress bars, completion charts, and timeline views
- Task Editor: Create, modify, and delete tasks with rich form interfaces
- Dependency Graph Visualization: Interactive network diagrams showing task relationships
Reasoning Insights:
- Decision Timeline: Step-by-step visualization of AI reasoning processes
- Alternative Path Analysis: View other approaches considered but not taken
- Reasoning Quality Scores: Metrics on reasoning depth, accuracy, and completeness
- Pattern Recognition: Identify common reasoning patterns and success factors
Artifact Management:
- Generated Content Gallery: Browse all files, code, and documentation created by AI agents
- Artifact Relationships: See how generated content relates to specific tasks and reasoning steps
- Version Control Integration: Track changes and evolution of generated artifacts
- Export & Sharing: Download artifacts and reasoning summaries for external use
MIT License - see LICENSE file for details.
Contributions are welcome! Please submit pull requests with appropriate tests and documentation.
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Meta Mind MCP Server - Advanced task orchestration for intelligent AI agents.