RAG Docs
This MCP server, developed by qpd-v, enables AI assistants to perform semantic search and retrieval of documentation using a vector database (Qdrant). It provides tools for adding documentation from URLs, searching through stored content, and listing sources. The server implements web scraping, text chunking, and embedding generation using either Ollama or OpenAI. By connecting AI capabilities with vector search technology, this implementation empowers AI assistants to quickly find relevant information within large document collections. It is particularly useful for applications requiring context-aware information retrieval, knowledge base augmentation, or any scenario where an AI system needs to efficiently access and reason about domain-specific documentation.
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 2026-09-28.
Scan details: Circle-IR · 2026-09-28 · 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/qpd-v/mcp-ragdocs
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve RAG Docs 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.