RAG
A cloud-based RAG server that provides document management and semantic search capabilities using OpenAI's embedding and chat APIs. The implementation features an in-memory vector store with cosine similarity search, document ingestion tools for adding and managing text content with metadata, search functionality with filtering support, and utility tools for database statistics and management. Built with persistence support through JSON serialization and designed for cloud-only operation without local vector databases, it's useful for building AI assistants that need to search and retrieve information from custom document collections, knowledge bases, or content repositories.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately. Last scanned 2026-09-19.
Scan details: Circle-IR · 2026-09-19 · Appeal
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- database
- Source
- PulseMCP
- Repository
- github.com/kalicyh/mcp-rag
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve 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.