MCP Embedding Storage

Example Use Cases

• 1

  Store documents or knowledge with automatically generated embeddings for later semantic retrieval.

• 2

  Perform vector-based semantic searches to find the most relevant sections of stored content.

• 3

  An MCP-accessible tool to integrate embedding storage and search into agent workflows (e.g., Claude for Desktop).

Description

This MCP server connects to the AI Embeddings API to split content into sections, generate embeddings, and store both content and embeddings in a database. It exposes tools and resources for storing content and performing semantic vector searches, and includes prompts for common operations. It is designed for integration with agent platforms (for example Claude for Desktop) to make knowledge storage and retrieval simple and programmatic.