Initial commit: Complete Hive distributed AI orchestration platform
This comprehensive implementation includes: - FastAPI backend with MCP server integration - React/TypeScript frontend with Vite - PostgreSQL database with Redis caching - Grafana/Prometheus monitoring stack - Docker Compose orchestration - Full MCP protocol support for Claude Code integration Features: - Agent discovery and management across network - Visual workflow editor and execution engine - Real-time task coordination and monitoring - Multi-model support with specialized agents - Distributed development task allocation 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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anthonyrawlins
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mcp-server/README.md
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# 🐝 Hive MCP Server
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Model Context Protocol (MCP) server that exposes the Hive Distributed AI Orchestration Platform to AI assistants like Claude.
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## Overview
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This MCP server allows AI assistants to:
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- 🤖 **Orchestrate Agent Tasks** - Assign development work across your distributed cluster
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- 📊 **Monitor Executions** - Track task progress and results in real-time
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- 🔄 **Manage Workflows** - Create and execute complex distributed pipelines
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- 📈 **Access Cluster Resources** - Get status, metrics, and performance data
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## Quick Start
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### 1. Install Dependencies
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```bash
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cd mcp-server
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npm install
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```
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### 2. Build the Server
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```bash
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npm run build
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```
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### 3. Configure Claude Desktop
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Add to your Claude Desktop configuration (`~/Library/Application Support/Claude/claude_desktop_config.json`):
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```json
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{
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"mcpServers": {
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"hive": {
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"command": "node",
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"args": ["/path/to/hive/mcp-server/dist/index.js"],
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"env": {
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"HIVE_API_URL": "http://localhost:8087",
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"HIVE_WS_URL": "ws://localhost:8087"
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}
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}
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}
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}
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```
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### 4. Restart Claude Desktop
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The Hive MCP server will automatically connect to your running Hive cluster.
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## Available Tools
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### Agent Management
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- **`hive_get_agents`** - List all registered agents with status
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- **`hive_register_agent`** - Register new agents in the cluster
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### Task Management
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- **`hive_create_task`** - Create development tasks for specialized agents
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- **`hive_get_task`** - Get details of specific tasks
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- **`hive_get_tasks`** - List tasks with filtering options
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### Workflow Management
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- **`hive_get_workflows`** - List available workflows
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- **`hive_create_workflow`** - Create new distributed workflows
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- **`hive_execute_workflow`** - Execute workflows with inputs
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### Monitoring
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- **`hive_get_cluster_status`** - Get comprehensive cluster status
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- **`hive_get_metrics`** - Retrieve Prometheus metrics
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- **`hive_get_executions`** - View workflow execution history
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### Coordination
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- **`hive_coordinate_development`** - Orchestrate complex multi-agent development projects
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## Available Resources
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### Real-time Cluster Data
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- **`hive://cluster/status`** - Live cluster status and health
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- **`hive://agents/list`** - Agent registry with capabilities
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- **`hive://tasks/active`** - Currently running and pending tasks
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- **`hive://tasks/completed`** - Recent task results and metrics
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### Workflow Data
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- **`hive://workflows/available`** - All configured workflows
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- **`hive://executions/recent`** - Recent workflow executions
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### Monitoring Data
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- **`hive://metrics/prometheus`** - Raw Prometheus metrics
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- **`hive://capabilities/overview`** - Cluster capabilities summary
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## Example Usage with Claude
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### Register an Agent
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```
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Please register a new agent in my Hive cluster:
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- ID: walnut-kernel-dev
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- Endpoint: http://walnut.local:11434
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- Model: codellama:34b
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- Specialization: kernel_dev
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```
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### Create a Development Task
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```
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Create a high-priority kernel development task to optimize FlashAttention for RDNA3 GPUs.
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The task should focus on memory coalescing and include constraints for backward compatibility.
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```
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### Coordinate Complex Development
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```
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Help me coordinate development of a new PyTorch operator that includes:
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1. CUDA/HIP kernel implementation (high priority)
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2. PyTorch integration layer (medium priority)
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3. Performance benchmarks (medium priority)
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4. Documentation and examples (low priority)
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5. Unit and integration tests (high priority)
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Use parallel coordination where possible.
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```
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### Monitor Cluster Status
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```
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What's the current status of my Hive cluster? Show me agent utilization and recent task performance.
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```
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## Environment Variables
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- **`HIVE_API_URL`** - Hive backend API URL (default: `http://localhost:8087`)
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- **`HIVE_WS_URL`** - Hive WebSocket URL (default: `ws://localhost:8087`)
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## Development
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### Watch Mode
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```bash
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npm run watch
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```
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### Direct Run
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```bash
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npm run dev
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```
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## Integration with Hive
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This MCP server connects to your running Hive platform and provides a standardized interface for AI assistants to:
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1. **Understand** your cluster capabilities and current state
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2. **Plan** complex development tasks across multiple agents
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3. **Execute** coordinated workflows with real-time monitoring
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4. **Optimize** task distribution based on agent specializations
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The server automatically handles task queuing, agent assignment, and result aggregation - allowing AI assistants to focus on high-level orchestration and decision-making.
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## Security Notes
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- The MCP server connects to your local Hive cluster
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- No external network access required
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- All communication stays within your development environment
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- Agent endpoints should be on trusted networks only
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---
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🐝 **Ready to let Claude orchestrate your distributed AI development cluster!**
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