Atlas Vector Search Docs
A vector search system for document retrieval using MongoDB Atlas Vector Search and Voyage AI embeddings, created by Pat Wendorf from MongoDB. The implementation ingests and chunks markdown documents with hierarchical headers, generates contextual embeddings using Voyage AI's API, and stores documents with embeddings in MongoDB collections with parent-child relationships. Built with FastMCP for integration with AI assistants like Claude Desktop, it enables semantic search across technical documentation and supports configurable vector dimensions, automatic quantization, and pre-filtering capabilities for efficient document discovery and retrieval-augmented generation workflows.
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/patw/avs-docs-mcp
- Author type
- human
- Last scanned
- 2026-09-19
- Updated
- 2026-09-19
Use via MCP
Resolve Atlas Vector Search 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.