Shared Knowledge RAG
Shared Knowledge MCP Server enables AI assistants to access and retrieve information from various vector stores, supporting RAG (Retrieval Augmented Generation) workflows. The implementation supports multiple vector store backends including HNSWLib, Weaviate, and others, with a flexible architecture that allows easy switching between them through environment variables. Built with TypeScript and LangChain, it provides a unified interface for knowledge retrieval regardless of the underlying storage technology, making it particularly valuable for applications that need to augment AI responses with domain-specific knowledge without requiring complex integration work for each vector database type.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/j5ik2o/shared-knowledge-mcp
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Shared Knowledge RAG from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.