- Migrated from HIVE branding to WHOOSH across all components - Enhanced backend API with new services: AI models, BZZZ integration, templates, members - Added comprehensive testing suite with security, performance, and integration tests - Improved frontend with new components for project setup, AI models, and team management - Updated MCP server implementation with WHOOSH-specific tools and resources - Enhanced deployment configurations with production-ready Docker setups - Added comprehensive documentation and setup guides - Implemented age encryption service and UCXL integration 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
🐝 WHOOSH MCP Server
Model Context Protocol (MCP) server that exposes the WHOOSH Distributed AI Orchestration Platform to AI assistants like Claude.
Overview
This MCP server allows AI assistants to:
- 🤖 Orchestrate Agent Tasks - Assign development work across your distributed cluster
- 📊 Monitor Executions - Track task progress and results in real-time
- 🔄 Manage Workflows - Create and execute complex distributed pipelines
- 📈 Access Cluster Resources - Get status, metrics, and performance data
Quick Start
1. Install Dependencies
cd mcp-server
npm install
2. Build the Server
npm run build
3. Configure Claude Desktop
Add to your Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json):
Production (Swarm Deployment):
{
"mcpServers": {
"whoosh": {
"command": "node",
"args": ["/path/to/whoosh/mcp-server/dist/index.js"],
"env": {
"WHOOSH_API_URL": "https://whoosh.home.deepblack.cloud",
"WHOOSH_WS_URL": "wss://whoosh.home.deepblack.cloud"
}
}
}
}
Development/Local Testing:
{
"mcpServers": {
"whoosh": {
"command": "node",
"args": ["/path/to/whoosh/mcp-server/dist/index.js"],
"env": {
"WHOOSH_API_URL": "http://localhost:8087",
"WHOOSH_WS_URL": "ws://localhost:8087"
}
}
}
}
4. Restart Claude Desktop
The WHOOSH MCP server will automatically connect to your running WHOOSH cluster.
Available Tools
Agent Management
whoosh_get_agents- List all registered agents with statuswhoosh_register_agent- Register new agents in the cluster
Task Management
whoosh_create_task- Create development tasks for specialized agentswhoosh_get_task- Get details of specific taskswhoosh_get_tasks- List tasks with filtering options
Workflow Management
whoosh_get_workflows- List available workflowswhoosh_create_workflow- Create new distributed workflowswhoosh_execute_workflow- Execute workflows with inputs
Monitoring
whoosh_get_cluster_status- Get comprehensive cluster statuswhoosh_get_metrics- Retrieve Prometheus metricswhoosh_get_executions- View workflow execution history
Coordination
whoosh_coordinate_development- Orchestrate complex multi-agent development projects
Available Resources
Real-time Cluster Data
whoosh://cluster/status- Live cluster status and healthwhoosh://agents/list- Agent registry with capabilitieswhoosh://tasks/active- Currently running and pending taskswhoosh://tasks/completed- Recent task results and metrics
Workflow Data
whoosh://workflows/available- All configured workflowswhoosh://executions/recent- Recent workflow executions
Monitoring Data
whoosh://metrics/prometheus- Raw Prometheus metricswhoosh://capabilities/overview- Cluster capabilities summary
Example Usage with Claude
Register an Agent
Please register a new agent in my WHOOSH cluster:
- ID: walnut-kernel-dev
- Endpoint: http://walnut.local:11434
- Model: codellama:34b
- Specialization: kernel_dev
Create a Development Task
Create a high-priority kernel development task to optimize FlashAttention for RDNA3 GPUs.
The task should focus on memory coalescing and include constraints for backward compatibility.
Coordinate Complex Development
Help me coordinate development of a new PyTorch operator that includes:
1. CUDA/HIP kernel implementation (high priority)
2. PyTorch integration layer (medium priority)
3. Performance benchmarks (medium priority)
4. Documentation and examples (low priority)
5. Unit and integration tests (high priority)
Use parallel coordination where possible.
Monitor Cluster Status
What's the current status of my WHOOSH cluster? Show me agent utilization and recent task performance.
Configuration
The MCP server connects to the WHOOSH backend using domain endpoints by default. You can customize this by setting environment variables:
Production (Default):
WHOOSH_API_URL-https://whoosh.home.deepblack.cloudWHOOSH_WS_URL-wss://whoosh.home.deepblack.cloud
Development/Local Testing:
WHOOSH_API_URL-http://localhost:8087WHOOSH_WS_URL-ws://localhost:8087
Additional Options:
WHOOSH_TIMEOUT- Request timeout in milliseconds (default:30000)
Copy .env.example to .env and modify as needed for your deployment.
Development
Watch Mode
npm run watch
Direct Run
npm run dev
Integration with WHOOSH
This MCP server connects to your running WHOOSH platform and provides a standardized interface for AI assistants to:
- Understand your cluster capabilities and current state
- Plan complex development tasks across multiple agents
- Execute coordinated workflows with real-time monitoring
- Optimize task distribution based on agent specializations
The server automatically handles task queuing, agent assignment, and result aggregation - allowing AI assistants to focus on high-level orchestration and decision-making.
Security Notes
- The MCP server connects to your local WHOOSH cluster
- No external network access required
- All communication stays within your development environment
- Agent endpoints should be on trusted networks only
🐝 Ready to let Claude orchestrate your distributed AI development cluster!