Raibid Labs Documentation

WS3_COMPLETION_SUMMARY

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Workstream 3 Completion Summary

MCP Resources & Tools Implementation

Status: COMPLETE ✅ Date: 2025-11-14 Integration Points: WS1 (MCP Server), WS2 (Hardware Detection), WS4 (Documentation)

Deliverables Completed

1. Type Definitions (src/types/)

  • ✅ resources.ts - MCP resource type definitions including URI patterns and descriptors
  • ✅ tools.ts - MCP tool type definitions with Zod validation schemas
  • ✅ spark.ts - Spark configuration types with data size parsing utilities

2. Hardware Resources (src/resources/hardware.ts)

Implemented 7 hardware resource endpoints that integrate with WS2:

  • ✅ dgx://hardware/specs - Complete hardware specifications
  • ✅ dgx://hardware/topology - Full system topology
  • ✅ dgx://hardware/gpus - GPU-specific details with NVLink info
  • ✅ dgx://hardware/cpu - CPU specifications
  • ✅ dgx://hardware/memory - Memory information
  • ✅ dgx://hardware/storage - Storage devices
  • ✅ dgx://hardware/network - Network interfaces

Integration: Uses getHardwareSnapshot() from WS2’s topology module with caching support.

3. System Capabilities Resource (src/resources/capabilities.ts + src/analyzers/capabilities.ts)

  • ✅ dgx://system/capabilities - Analyzed system capabilities
  • ✅ Capability analyzer that provides:
    • Hardware summary (CPU cores, memory, GPU count, storage)
    • Spark recommendations (executors, cores, memory, partitions)
    • GPU capabilities (RAPIDS support, recommended config)
    • Framework support detection (Spark, RAPIDS, TensorFlow, PyTorch)
    • Performance estimates (throughput, compute TFLOPS, network/storage bandwidth)
    • Tailored recommendations based on detected hardware

4. Documentation Resources (src/resources/docs.ts)

  • ✅ dgx://docs/spark/{topic} - Dynamic documentation resources
  • ✅ Integration with WS4 documentation loader
  • ✅ Topic listing and routing
  • ✅ Markdown content serving

5. MCP Tools (src/tools/)

Tool 1: GPU Availability Checker (gpu-availability.ts)

check_gpu_availability(minMemoryGB?, minUtilization?)
  • Real-time GPU status from WS2
  • Available/busy GPU classification
  • Memory and utilization tracking
  • Job placement recommendations

Tool 2: Spark Config Generator (spark-config.ts)

get_optimal_spark_config(workloadType, dataSize, numExecutors?, executorMemory?, useGPU?)
  • Integrates with WS2’s Spark optimizer
  • Generates optimized configurations for ETL, ML training/inference, analytics, streaming
  • Provides spark-submit command
  • Hardware-aware recommendations

Tool 3: Documentation Search (search-docs.ts)

search_documentation(query, limit?, topics?)
  • Integrates with WS4’s search functionality
  • Relevance scoring
  • Contextual excerpts
  • Search suggestions

Tool 4: Resource Estimator (estimate-resources.ts)

estimate_resources(description, dataSize?, computeType?)
  • NLP-based workload detection
  • Resource requirement estimation
  • Feasibility analysis
  • Recommendations based on system capabilities

Tool 5: System Health Checker (system-health.ts)

get_system_health(verbose?)
  • Real-time health monitoring
  • Component-level status (CPU, memory, GPU, storage, network)
  • Alert generation with severity levels
  • Health summaries and recommendations

6. Tool Infrastructure

  • ✅ validation.ts - Zod-based argument validation
  • ✅ index.ts - Tool registry with unified call interface

7. MCP Server Integration (src/server.ts)

Updated main server to:

  • ✅ Register all resources via listAllResources()
  • ✅ Handle resource reads via readResource(uri)
  • ✅ Register all tools via listAllTools()
  • ✅ Handle tool calls via callTool(name, args)
  • ✅ Comprehensive error handling

API Surface

Resources (12 total)

  1. dgx://server/info - Server metadata
  2. dgx://hardware/specs - Hardware specs
  3. dgx://hardware/topology - System topology
  4. dgx://hardware/gpus - GPU details
  5. dgx://hardware/cpu - CPU info
  6. dgx://hardware/memory - Memory info
  7. dgx://hardware/storage - Storage info
  8. dgx://hardware/network - Network info
  9. dgx://system/capabilities - System capabilities analysis 10-12. dgx://docs/spark/* - Documentation (dynamic, from WS4)

Tools (5 total)

  1. check_gpu_availability - GPU status and recommendations
  2. get_optimal_spark_config - Spark configuration generation
  3. search_documentation - Documentation search
  4. estimate_resources - Resource requirement estimation
  5. get_system_health - System health monitoring

Integration Summary

With WS1 (MCP Server Foundation)

  • Registered resources with ListResourcesRequestSchema handler
  • Registered tools with ListToolsRequestSchema handler
  • Implemented ReadResourceRequestSchema handler
  • Implemented CallToolRequestSchema handler
  • All handlers include error handling and logging

With WS2 (Hardware Detection System)

  • Uses getHardwareSnapshot() from topology module
  • Leverages hardware caching for performance
  • Integrates with Spark optimizer from WS2
  • Accesses GPU detection for real-time availability

With WS4 (Documentation System)

  • Uses getDocumentationResourceList() for resource discovery
  • Uses loadDocumentationResource() for content serving
  • Uses handleSearchTool() for search functionality

Testing Commands

# Build project
npm run build
 
# List all resources
echo '{"jsonrpc":"2.0","id":1,"method":"resources/list"}' | node dist/index.js
 
# Read hardware specs
echo '{"jsonrpc":"2.0","id":2,"method":"resources/read","params":{"uri":"dgx://hardware/specs"}}' | node dist/index.js
 
# Read system capabilities
echo '{"jsonrpc":"2.0","id":3,"method":"resources/read","params":{"uri":"dgx://system/capabilities"}}' | node dist/index.js
 
# List all tools
echo '{"jsonrpc":"2.0","id":4,"method":"tools/list"}' | node dist/index.js
 
# Check GPU availability
echo '{"jsonrpc":"2.0","id":5,"method":"tools/call","params":{"name":"check_gpu_availability","arguments":{"minMemoryGB":8}}}' | node dist/index.js
 
# Generate Spark config
echo '{"jsonrpc":"2.0","id":6,"method":"tools/call","params":{"name":"get_optimal_spark_config","arguments":{"workloadType":"ml-training","dataSize":"100GB","useGPU":true}}}' | node dist/index.js
 
# Search documentation
echo '{"jsonrpc":"2.0","id":7,"method":"tools/call","params":{"name":"search_documentation","arguments":{"query":"GPU memory","limit":5}}}' | node dist/index.js
 
# Estimate resources
echo '{"jsonrpc":"2.0","id":8,"method":"tools/call","params":{"name":"estimate_resources","arguments":{"description":"Train 1B parameter model","dataSize":"500GB","computeType":"gpu"}}}' | node dist/index.js
 
# Check system health
echo '{"jsonrpc":"2.0","id":9,"method":"tools/call","params":{"name":"get_system_health","arguments":{"verbose":true}}}' | node dist/index.js

Files Created/Modified

New Files (18 total)

  1. src/types/resources.ts
  2. src/types/tools.ts
  3. src/types/spark.ts
  4. src/analyzers/capabilities.ts
  5. src/resources/hardware.ts
  6. src/resources/capabilities.ts
  7. src/resources/docs.ts
  8. src/resources/index.ts
  9. src/tools/gpu-availability.ts
  10. src/tools/spark-config.ts
  11. src/tools/search-docs.ts
  12. src/tools/estimate-resources.ts
  13. src/tools/system-health.ts
  14. src/tools/validation.ts
  15. src/tools/index.ts

Modified Files

  1. src/server.ts - Integrated resource and tool handlers

Completion Criteria Met

  • All hardware resources implemented and tested
  • System capabilities resource with intelligent analysis
  • Documentation resources serving content from WS4
  • GPU availability tool working with real-time detection
  • Spark config tool generating valid configs using WS2 optimizer
  • All additional tools implemented (search, estimate, health)
  • Resource caching via WS2 integration
  • Tool input validation with Zod schemas
  • Error handling comprehensive
  • MCP protocol compliance verified
  • Integration with WS1 MCP server complete
  • Integration with WS2 hardware detection complete
  • Integration with WS4 documentation complete

Next Steps for WS5 (Intelligence Layer)

WS5 can now enhance:

  1. get_optimal_spark_config - Add ML-based optimization
  2. System capabilities - Add predictive analytics
  3. Resource estimation - Add historical data analysis
  4. Health monitoring - Add anomaly detection

WS5 should check for: swarm/dgx-mcp/ws-3/complete before proceeding.

Memory Keys to Store

# Mark resources complete
swarm/dgx-mcp/ws-3/resources-complete
 
# Mark tools complete
swarm/dgx-mcp/ws-3/tools-complete
 
# Mark WS3 complete
swarm/dgx-mcp/ws-3/complete
 
# API details
{
  "resources": 12,
  "tools": 5,
  "integrations": ["WS1-MCP-Server", "WS2-Hardware-Detection", "WS4-Documentation"],
  "validation": "Zod",
  "protocol": "MCP-1.0"
}

Workstream 3 Implementation Complete ✅

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