Enhanced Data Retrieval System
Standardizes AI context sharing by integrating organizational knowledge sources for domain-aware AI assistance in enterprise development workflows.
About
This system implements the Model Context Protocol to provide standardized AI context sharing by integrating diverse organizational knowledge sources, including GitHub repositories and internal documentation. Utilizing OAuth 2.1 for secure access, it employs vector-based semantic search with embeddings and optimized 1500-token context chunking to deliver highly relevant, domain-aware AI assistance. The server features a parallel retrieval strategy and streamable HTTP transport, engineered for performance with sub-500ms response times and robust contextualization capabilities for enterprise development workflows.
Key Features
- Optimized 1500-token context chunking with parallel retrieval
- Vector Embeddings for semantic search (ChromaDB, Sentence Transformers)
- GitHub Integration with repository data retrieval
- OAuth 2.1 Security for GitHub access
- MCP Protocol Compliance (JSON-RPC 2.0 over Streamable HTTP)
- 0 GitHub stars
Use Cases
- Enabling domain-aware AI assistance for enterprise development workflows
- Standardizing AI context sharing across organizational knowledge sources
- Contextualizing GitHub repository information for AI models
