Connects Claude to the Vectorize.io platform for document processing and vector search operations. Exposes three core tools: vector retrieval for semantic search across your pipeline documents, text extraction that converts any file format to chunked Markdown, and deep research generation that combines your data with optional web search. Requires Vectorize organization credentials and pipeline ID. Reach for this when you need Claude to search through your existing document collections, extract structured text from uploads, or generate comprehensive research reports using your proprietary data sources alongside web results.
A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction.
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 one-click installation, click one of the install buttons below:
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}"
}
}
}
}
{
"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"
}
}
}
}
Perform vector search and retrieve documents (see official API):
{
"name": "retrieve",
"arguments": {
"question": "Financial health of the company",
"k": 5
}
}
Extract text from a document and chunk it into Markdown format (see official API):
{
"name": "extract",
"arguments": {
"base64document": "base64-encoded-document",
"contentType": "application/pdf"
}
}
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
}
}
npm install
npm run dev
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