MCP RAG Development Status - TerrenceMcGuinness-NOAA/global-workflow GitHub Wiki
TerrenceMcGuinness-NOAA: We have serveral MCP servies running for global-workflow: enahced-rag, rag, simle, and wsl. Let's go through all the tools for each and check which ones are operable and not templated. Check the code for each and see what steps need to be taken. It appears we still not have the database connection configured and working to finish some of the tools development. Let's create a table for each of these MCP servers each with paragraph for each table with a breif discription and how the relate to others along with the list of each tool for every table with a column for wihich ones are trurly operable and which onees needs furrther development or implementation.
GitHub Copilot: I'll help you analyze the MCP services for global-workflow. Let me first search for the MCP server implementations and their tools to understand the current state. Let me search more specifically for MCP server implementations and check the WSL server as well: Now let me check for WSL-specific files: Let me look at the main MCP server files to understand the current structure: Now let me check the RAG-enhanced server: Let me check the enhanced RAG server: Let me check the simple RAG server: Now let me check if there's a WSL-specific server or if it's in Python: Now let me examine the tools defined in each server. Let me get the complete tool lists: Let me read the complete tool definitions for each server to create the comparison tables: Now let me get the complete tool definitions for the RAG server: Now let me get the tool definitions for the Enhanced RAG server: Let me get the simple RAG server tools: Now let me check the Python server tools: Now let me check if there are any database initialization or configuration files to understand the current state: Perfect! Now I have enough information to create comprehensive comparison tables for each MCP server. Let me create a comprehensive analysis:
Based on my analysis of the MCP servers, here are the detailed comparison tables for each of the four MCP services for global-workflow:
1. Simple MCP Server (mcp-server.js)
Description: This is the basic Node.js MCP server that provides core global-workflow functionality without RAG enhancements. It serves as the foundation for the other servers and offers straightforward workflow information access. This server focuses on basic repository navigation and documentation without advanced search capabilities.
| Tool Name | Description | Status | Implementation Notes |
|---|---|---|---|
get_workflow_structure |
Get structure/overview of global workflow system | ✅ Operational | Fully implemented with hardcoded responses |
list_job_scripts |
List all available job scripts in workflow | ✅ Operational | Working directory traversal implementation |
get_system_configs |
Get HPC system configuration information | ✅ Operational | Hardcoded system info, functional |
explain_component |
Explain specific workflow components | ✅ Operational | Basic component explanations available |
2. RAG-Enhanced MCP Server (mcp-server-rag.js)
Description: This server extends the basic server with Retrieval-Augmented Generation capabilities using ChromaDB and local embeddings. It includes all tools from the simple server plus advanced RAG features. The server has fallback mechanisms when vector database connections fail, ensuring basic functionality remains available.
| Tool Name | Description | Status | Implementation Notes |
|---|---|---|---|
get_workflow_structure |
Basic workflow structure info | ✅ Operational | Inherited from simple server |
list_job_scripts |
List workflow job scripts | ✅ Operational | Inherited from simple server |
get_system_configs |
HPC system configurations | ✅ Operational | Inherited from simple server |
explain_component |
Basic component explanations | ✅ Operational | Inherited from simple server |
search_documentation |
Semantic search with RAG | ⚠️ Partially Operational | Works with local embeddings when ChromaDB fails |
explain_with_context |
RAG-enhanced explanations | ⚠️ Partially Operational | Requires vector database for full functionality |
find_similar_code |
Vector similarity code search | ⚠️ Partially Operational | Has local fallback but limited without DB |
get_operational_guidance |
HPC operational procedures | ⚠️ Partially Operational | Template responses when RAG unavailable |
analyze_dependencies |
Workflow dependency analysis | ⚠️ Partially Operational | Basic analysis without graph database |
3. Enhanced RAG MCP Server (mcp-server-enhanced-rag.js)
Description: This advanced server integrates Hugging Face models and APIs with local RAG capabilities. It's designed to leverage external ML models for documentation enhancement, code analysis, and automated content generation. This server represents the most sophisticated approach but requires external API access and configuration.
| Tool Name | Description | Status | Implementation Notes |
|---|---|---|---|
search_documentation |
Enhanced semantic search with HF integration | ⚠️ Needs Development | Requires HF API configuration |
enhance_documentation_with_hf |
HF papers/datasets integration | ❌ Template Only | Requires HuggingFace API setup |
find_similar_implementations |
HF code model integration | ❌ Template Only | Needs HF code model configuration |
generate_documentation |
HF text generation for docs | ❌ Template Only | Requires HF text generation API |
get_workflow_structure |
Basic workflow info | ✅ Operational | Basic functionality works |
list_job_scripts |
Job script listing | ✅ Operational | Basic functionality works |
explain_workflow_component |
Component explanations | ✅ Operational | Basic functionality works |
analyze_workflow_dependencies |
Dependency analysis | ⚠️ Partially Operational | Limited without full HF integration |
4. Simple RAG Server (simple-rag-server.js)
Description: This is a testing/demonstration server that uses basic keyword matching instead of vector embeddings. It's designed for environments where machine learning models aren't available but some RAG-like functionality is needed. It loads pre-processed knowledge bases and provides search capabilities using simple text matching algorithms.
| Tool Name | Description | Status | Implementation Notes |
|---|---|---|---|
search_documentation |
Keyword-based document search | ✅ Operational | Uses simple keyword matching, works well |
explain_component |
Component explanations with examples | ✅ Operational | Functional with knowledge base loaded |
list_workflow_jobs |
List workflow jobs with filtering | ✅ Operational | Good filtering and categorization |
get_knowledge_stats |
Knowledge base statistics | ✅ Operational | Shows loaded chunks and documents |
get_documentation_references |
External documentation links | ✅ Operational | Provides structured reference links |
5. Python MCP Server (mcp-server.py)
Description: This is a Python-based implementation that focuses on basic workflow information without RAG capabilities. It's designed for environments where Python is preferred over Node.js and provides a clean, simple interface to workflow information. This server could serve as the foundation for Python-based RAG implementations.
| Tool Name | Description | Status | Implementation Notes |
|---|---|---|---|
get_workflow_info |
General workflow information | ✅ Operational | Comprehensive workflow documentation |
list_job_scripts |
List available job scripts | ✅ Operational | File system traversal working |
get_config_info |
System configuration info | ✅ Operational | Basic system information available |
Database and Infrastructure Status
Current State Analysis:
- ✅ ChromaDB: Local database exists with 978 chunks processed
- ✅ Embeddings: Pre-computed embeddings available (all-MiniLM-L6-v2, 384 dimensions)
- ✅ Knowledge Base: JSON files with chunks and documents are present
- ⚠️ Database Connection: ChromaDB server may not be running (connection issues noted in logs)
- ❌ HuggingFace Integration: Configuration files missing, API access not configured
- ⚠️ Vector Search: Working in fallback mode with local embeddings
Required Steps for Full Functionality
Immediate Actions Needed:
-
Start ChromaDB Server:
cd /path/to/mcp_server_node/knowledge-base chroma run --host localhost --port 8000 --path ./chroma_db -
Configure HuggingFace Integration:
- Create
hf_integration/config/huggingface.jsonwith API credentials - Set up HF API token in environment variables
- Create
-
Test Database Connections:
- Verify ChromaDB connectivity
- Test embedding model loading
- Validate knowledge base integrity
Development Priorities:
- High Priority: Fix ChromaDB connection for RAG server
- Medium Priority: Complete HuggingFace integration implementation
- Low Priority: Add WSL-specific server if needed for Windows development
The Simple RAG Server is currently the most reliable for demonstration purposes, while the RAG-Enhanced Server offers the best functionality when properly configured.