vectorize-io/vectorize-mcp-server

Overview

An MCP server that integrates Vectorize's vector database with AI agents and tools. It provides a standardized interface for storing, searching, and managing vector embeddings through the Model Context Protocol.

Best for

Best for
Developers building AI agents that need persistent vector memory

Use cases

• Add semantic memory to AI agents via vector search
• Store and retrieve document embeddings for RAG pipelines
• Manage vector collections and indexes programmatically

Notes

An MCP server that integrates Vectorize’s vector database with AI agents and tools. It provides a standardized interface for storing, searching, and managing vector embeddings through the Model Context Protocol.

108 stars on GitHub. Last updated 2026-05-11. Licensed MIT.

Use cases

• Add semantic memory to AI agents via vector search
• Store and retrieve document embeddings for RAG pipelines
• Manage vector collections and indexes programmatically

Pros

• Simple MCP integration for existing AI workflows
• Open source with active community (108 stars)
• JavaScript implementation easy to extend

Cons

• Requires Vectorize service account and API key
• Limited to Vectorize’s ecosystem and pricing
• No built-in embedding generation

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