Vectorize

Vectorize is an MCP server that connects AI assistants directly to the Vectorize data platform for vector retrieval, file extraction, and deep research workflows. Designed for developers, data engineers, and AI practitioners using clients like Claude, Cursor, Windsurf, or VS Code, it exposes pipeline capabilities through standardized Model Context Protocol tools. Users can query their pre-configured Vectorize data pipelines to return top matching text chunks, run vector searches across corporate datasets, and extract text from base64-encoded files like PDFs directly into formatted Markdown chunks. Additionally, the server provides an integrated deep research capability that allows AI agents to synthesize comprehensive, sourced research reports derived from enterprise pipeline data, with an optional toggle to incorporate live web search results. By bridging the Vectorize API with interactive AI environments, the server enables contextual answering and document parsing without manual data transfer.

Category: AI Memory & Context

Tags: markdown, rag, research, text-chunking, vector-database

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How to install and configure Vectorize

To install and configure the Vectorize MCP server in your MCP client (such as Claude Desktop, Cursor, Windsurf, or Cline): 1. Obtain your Organization ID, API Token, and Pipeline ID from your Vectorize account. 2. Open your client's MCP configuration file (such as mcpServers in Claude Desktop or User Settings in VS Code). 3. Add the server definition with the required environment variables: json { "mcpServers": { "vectorize": { "command": "npx", "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"], "env": { "VECTORIZE_ORG_ID": "your-org-id", "VECTORIZE_TOKEN": "your-token", "VECTORIZE_PIPELINE_ID": "your-pipeline-id" } } } } 4. Save the configuration and restart your client to initialize the server tools.

What you can do with Vectorize

• Querying pre-indexed Vectorize pipelines using natural language questions to retrieve the top-k matching document chunks for grounded context. - Converting base64-encoded PDF or text documents into structured Markdown format using the automated text chunking tool. - Generating private deep research reports synthesizing enterprise pipeline data with optional live web search inclusion. - Augmenting developer workflows in VS Code, Cursor, or Claude Desktop with direct access to proprietary vector search indexes.

Key facts

• https://github.com/vectorize-io/vectorize-mcp-server
• AI Memory & Context, Files, Documents & PDFs, Web Search & Research
• markdown, rag, research, text-chunking, vector-database

Part of MCP Servers

What can Vectorize MCP do?

Vectorize MCP provides three main functions through standard tools. It executes semantic vector retrieval against pre-configured Vectorize pipelines, extracts and converts raw documents like PDFs into chunked Markdown format, and produces sourced deep research reports using internal pipeline data combined with optional live web search results.

Which MCP clients work with Vectorize?

Vectorize works with any client that implements the Model Context Protocol. Supported and tested environments include VS Code, VS Code Insiders, Claude Desktop, Cursor, Windsurf, and Cline, configured using standard command and environment variable definitions.

How do I configure credentials for the Vectorize server?

You must supply three environment variables to run the server: VECTORIZE_ORG_ID, VECTORIZE_TOKEN, and VECTORIZE_PIPELINE_ID. These can be defined in your client JSON settings under the env key or exported in your local shell environment prior to starting the npx process.

What tools are included in the Vectorize MCP server?

The server exposes three primary tools: retrieve for querying Vectorize pipeline data and returning chunks, extract for parsing base64 documents into Markdown format, and deep-research for running automated research reports over your indexed data.