RAG Documentation
This RAG documentation MCP server, developed by Rahul Retnan as a fork of qpd-v's original project, enables AI assistants to augment their responses with relevant documentation context. Built with TypeScript and integrating Qdrant for vector search, it offers tools for semantic documentation retrieval, source management, and automated processing of new content. The implementation focuses on enhancing AI capabilities through context-aware documentation access, with features like natural language querying and efficient queue management. It's particularly useful for developers building documentation-aware AI systems, enabling use cases such as context-enhanced chatbots, semantic documentation search, and automated knowledge base augmentation without directly handling vector database complexities.
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/rahulretnan/mcp-ragdocs
- Author type
- human
- Last scanned
- 2026-09-28
- Updated
- 2026-09-28
Use via MCP
Resolve RAG Documentation 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.