Rust Local RAG
This MCP server provides local document search and retrieval capabilities using Rust for high-performance PDF processing and semantic search. Built by Mehmet Koray Sariteke, it integrates with Ollama's nomic-embed-text model for generating embeddings and uses poppler's pdftotext for text extraction, automatically indexing PDF documents from a specified directory and storing embeddings locally for fast retrieval. The implementation offers tools for semantic document search with configurable result limits, document listing, and system statistics, making it valuable for AI assistants that need to search through local document collections, research papers, or knowledge bases without relying on external services.
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
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/ksaritek/rust-local-rag
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Rust Local 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.