RAG Context
This MCP server provides persistent memory and context management for AI assistants using local vector storage and SQLite database, enabling semantic search and indexed retrieval of stored information. Built with TypeScript using Vectra for vector similarity search and Xenova/all-MiniLM-L6-v2 for local text embeddings, it offers two core tools: setContext for storing information with automatic vectorization and metadata support, and getContext for retrieving relevant context through hybrid semantic search with configurable similarity thresholds. The implementation runs entirely locally with no external API calls, storing data in a user-specified directory with SQLite for reliable persistence and vector indices for efficient similarity search, making it valuable for AI assistants that need to remember user preferences, project configurations, coding patterns, and other contextual information across conversations while maintaining complete privacy and data control.
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/notbnull/mcp-rag-context
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve RAG Context 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.