Qdrant Docs Rag
This MCP server, developed by Hannes Rudolph, enables AI assistants to augment their responses with relevant documentation context through vector-based search and retrieval. Built as a fork of qpd-v's original implementation, it integrates with OpenAI for embeddings generation and Qdrant for vector storage. The server provides tools for adding documentation from URLs, performing semantic searches, extracting links, and managing a processing queue. By connecting AI capabilities with efficient vector search of documentation, this implementation allows AI systems to enhance their knowledge with domain-specific information in real-time. It is particularly useful for building documentation-aware AI assistants, implementing semantic documentation search, and creating context-aware developer tools that require access to up-to-date technical information.
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
- ai-ml
- Source
- PulseMCP
- Repository
- github.com/hannesrudolph/mcp-ragdocs
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Qdrant Docs 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.