What can you do with this server?

The Vectorize MCP Server integrates with Vectorize to provide advanced vector retrieval and text extraction capabilities: * **Retrieve documents**: Perform vector search to retrieve documents from a Vectorize pipeline based on a query and desired number of results. * **Text extraction and chunking**: Extract text from various document formats and chunk it into Markdown format. * **Deep Research**: Generate research reports from your pipeline, with an option to include web search results.

Which integrations are available for this server?

Supports text extraction and chunking of documents into [Markdown](/mcp/servers/integrations/markdown) format

Vectorize MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction.

Installation

Running with npx

export VECTORIZE_ORG_ID=YOUR_ORG_ID export VECTORIZE_TOKEN=YOUR_TOKEN export VECTORIZE_PIPELINE_ID=YOUR_PIPELINE_ID npx -y @vectorize-io/vectorize-mcp-server@latest

VS Code Installation

For one-click installation, click one of the install buttons below:

Install with NPX in VS Code Install with NPX in VS Code Insiders

Manual Installation

For the quickest installation, use the one-click install buttons at the top of this section.

To install manually, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open User Settings (JSON).

{ "mcp": { "inputs": [ { "type": "promptString", "id": "org_id", "description": "Vectorize Organization ID" }, { "type": "promptString", "id": "token", "description": "Vectorize Token", "password": true }, { "type": "promptString", "id": "pipeline_id", "description": "Vectorize Pipeline ID" } ], "servers": { "vectorize": { "command": "npx", "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"], "env": { "VECTORIZE_ORG_ID": "${input:org_id}", "VECTORIZE_TOKEN": "${input:token}", "VECTORIZE_PIPELINE_ID": "${input:pipeline_id}" } } } } }

Optionally, you can add the following to a file called .vscode/mcp.json in your workspace to share the configuration with others:

{ "inputs": [ { "type": "promptString", "id": "org_id", "description": "Vectorize Organization ID" }, { "type": "promptString", "id": "token", "description": "Vectorize Token", "password": true }, { "type": "promptString", "id": "pipeline_id", "description": "Vectorize Pipeline ID" } ], "servers": { "vectorize": { "command": "npx", "args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"], "env": { "VECTORIZE_ORG_ID": "${input:org_id}", "VECTORIZE_TOKEN": "${input:token}", "VECTORIZE_PIPELINE_ID": "${input:pipeline_id}" } } } }

Related MCP server: Scrapezy

Configuration on Claude/Windsurf/Cursor/Cline

{ "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" } } } }

Tools

Retrieve documents

Perform vector search and retrieve documents (see official API):

{ "name": "retrieve", "arguments": { "question": "Financial health of the company", "k": 5 } }

Text extraction and chunking (Any file to Markdown)

Extract text from a document and chunk it into Markdown format (see official API):

{ "name": "extract", "arguments": { "base64document": "base64-encoded-document", "contentType": "application/pdf" } }

Deep Research

Generate a Private Deep Research from your pipeline (see official API):

{ "name": "deep-research", "arguments": { "query": "Generate a financial status report about the company", "webSearch": true } }

Development

npm install npm run dev

Release

Change the package.json version and then:

git commit -am "x.y.z" git tag x.y.z git push origin git push origin --tags

Contributing

Fork the repository
Create your feature branch
Submit a pull request

Vectorize