KBDB
A PostgreSQL-backed RAG (Retrieval-Augmented Generation) server that provides semantic search across document embeddings using pgvector for vector similarity operations. Built with FastMCP and OpenAI's embedding API, it supports multiple search modalities including question-answer pairs, semantic similarity, style-based clustering, and code similarity search, each optimized with different embedding prefixes and distance metrics (cosine, inner product, L2). The system stores documents in chunks with pre-computed embeddings and exposes specialized search tools for different use cases, making it valuable for knowledge base applications, documentation search, and content discovery workflows where different types of semantic matching are required.
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
- database
- Source
- PulseMCP
- Repository
- github.com/luxter77/mcp-kbdb
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve KBDB 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.