RAG Document Search
A RAG server that provides document embedding, semantic search, and citation generation capabilities using ChromaDB as the vector store and HuggingFace models for embeddings and reranking. The implementation supports PDF document ingestion from local files or URLs, creates searchable collections with configurable chunking strategies, and includes cross-encoder reranking for improved retrieval quality. Features include multi-collection search, document metadata management, automatic citation generation for LLM responses, and local model storage for offline operation, making it useful for building knowledge bases, research assistants, and document Q&A systems that require accurate source attribution.
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/nsantra/rag-mcp-server
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve RAG Document Search 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.