Integrate Vectorize for Advanced Research and Data Extraction
An MCP server for Vectorize offering vector search, document extraction, and deep-research generation.
Why it matters
Leverage Vectorize.io's advanced capabilities for sophisticated vector search, extracting text from any file format into Markdown, and conducting private deep research with optional web search integration.
Outcomes
What it gets done
Perform vector search and retrieve relevant documents.
Extract text from various file formats and convert to Markdown.
Generate private deep research reports, optionally including web search results.
Integrate seamlessly with Vectorize.io pipelines for enhanced data analysis.
Install
Add it to your toolbox
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/vb-vectorize | bash Capabilities
Tools your agent gets
Performs vector search and retrieves documents from the pipeline
Extracts text from any document format and chunks it into Markdown format
Generates a private deep research report from your pipeline with optional web search
Overview
Vectorize MCP Server
An MCP server for Vectorize offering vector search, document-to-Markdown extraction, and deep-research report generation. Use it when an MCP client needs to search, extract, or research against your own Vectorize pipeline.
What it does
An MCP server that integrates with Vectorize for vector retrieval, document text extraction, and deep research: it lets an MCP client perform vector search against a Vectorize pipeline, extract and chunk any document into Markdown, and generate a private deep-research report from that pipeline's data.
When to use - and when NOT to
Use this when an MCP client needs to search a Vectorize knowledge pipeline for relevant documents, convert an uploaded file (e.g. a PDF) into chunked Markdown text, or generate a synthesized research report grounded in your own pipeline data - for example "Generate a financial status report about the company" with web search enabled. It requires an existing Vectorize organization, an access token, and a configured pipeline - it is not itself a standalone vector database, but rather a connector to Vectorize's already-hosted pipelines and their indexed content.
Capabilities
retrieve: vector search against a pipeline, e.g.{"question": "Financial health of the company", "k": 5}to get the top 5 most relevant matching documents from the pipeline.extract: text extraction and chunking of any document into Markdown, given abase64documentand itscontentType(e.g.application/pdf), useful for turning uploaded files into pipeline-ready text.deep-research: generates a Private Deep Research report from the pipeline, with an optionalwebSearchflag to include live web search alongside the pipeline's own indexed data in the synthesis.
How to install
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
For Claude, Windsurf, Cursor, or 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"
}
}
}
}
VS Code has one-click install buttons, or a manual mcp block in User Settings (JSON) or a shared .vscode/mcp.json, prompting for the organization ID, token, and pipeline ID as password-masked input fields rather than plain hardcoded values.
Who it's for
Developers who already have a Vectorize pipeline and want an MCP client (Claude, Cursor, Windsurf, Cline, or VS Code) to run vector search, extract and chunk documents, or generate deep-research reports directly against that pipeline's data. Each tool maps directly to one of Vectorize's own hosted APIs - retrieval, extraction, and deep research - so behavior and result quality match what the same request would return through Vectorize's own API documentation, just callable as an MCP tool instead of a raw HTTP request.
Source README
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:
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}"
}
}
}
}
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
Discussion
Questions & comments · 0
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