by vectorize-io
Connect Claude with Vectorize.io's vector database to extract text from images and enable advanced retrieval for researc
Connects Claude to Vectorize.io's vector database services for document retrieval and text extraction. Requires Vectorize API credentials to access their vector search capabilities.
Vectorize is an official MCP server published by vectorize-io that provides AI assistants with tools and capabilities via the Model Context Protocol. Connect Claude with Vectorize.io's vector database to extract text from images and enable advanced retrieval for researc It is categorized under ai ml, analytics data.
You can install Vectorize in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Vectorize is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Vectorize is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Useful MCP listing: Vectorize is the kind of server we cite when onboarding engineers to host + tool permissions.
Vectorize has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Vectorize reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend Vectorize for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Vectorize benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Vectorize is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
Strong directory entry: Vectorize surfaces stars and publisher context so we could sanity-check maintenance before adopting.
I recommend Vectorize for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: Vectorize surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction.
<a href="https://glama.ai/mcp/servers/pxwbgk0kzr"> <img width="380" height="200" src="https://glama.ai/mcp/servers/pxwbgk0kzr/badge" alt="Vectorize MCP server" /> </a>
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
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.