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

How to use

Install

npx -y @vectorize-io/vectorize-mcp-server@latest

Tools exposed

• retrieve
• extract
• deep-research

Tested with

Cursor, Windsurf, Cline, VS Code

Example client config

{\n  "mcpServers": {\n    "vectorize": {\n      "command": "npx",\n      "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],\n      "env": {\n        "VECTORIZE_ORG_ID": "your-org-id",\n        "VECTORIZE_TOKEN": "your-token",\n        "VECTORIZE_PIPELINE_ID": "your-pipeline-id"\n      }\n    }\n  }\n}

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

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.