Vectorize
โ๏ธ ๐ - [Vectorize](https://vectorize.io) MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
What is Vectorize?
Vectorize is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to โ๏ธ ๐ - [vectorize](https://vectorize.io) mcp server for advanced retrieval, private deep research, anything-to-markdown file extraction and text chunking.
โ๏ธ ๐ - [Vectorize](https://vectorize.io) MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
This server falls under the Search & Data Extraction category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- MCP protocol support
Use Cases
Maintainer
Works with
Installation
NPM
npx -y @vectorize-io/vectorize-mcp-serverManual Installation
npx -y @vectorize-io/vectorize-mcp-serverConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Vectorize
The Vectorize MCP server connects AI assistants to the Vectorize.io RAG platform, giving them three powerful capabilities: semantic vector search over your document pipelines, document text extraction and Markdown conversion from any file format, and private deep research that synthesises answers from your own indexed knowledge base. By linking Claude or another MCP client to your Vectorize organization, you can ground AI responses in proprietary documents, run multi-document research sessions without sending data to public search engines, and convert PDFs, Office files, or other formats to structured Markdown on demand.
Prerequisites
- A Vectorize.io account with at least one pipeline configured (sign up at vectorize.io)
- VECTORIZE_ORG_ID โ your organization identifier from the Vectorize dashboard
- VECTORIZE_TOKEN โ your API authentication token
- VECTORIZE_PIPELINE_ID โ the ID of the pipeline you want to query
- Node.js 18+ and npx, or an MCP client that can run npx commands
Create a Vectorize pipeline
Log into vectorize.io, create an organization, and set up at least one document pipeline. Upload your documents or connect a data source. Copy your Org ID, API token, and Pipeline ID from the dashboard.
Add the server to your MCP client configuration
Edit your MCP client config to include the Vectorize server with the three required environment variables. All three must be present or the server will fail to start.
{
"mcpServers": {
"vectorize": {
"command": "npx",
"args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
"env": {
"VECTORIZE_ORG_ID": "your-org-id",
"VECTORIZE_TOKEN": "your-api-token",
"VECTORIZE_PIPELINE_ID": "your-pipeline-id"
}
}
}
}Restart your MCP client
Save the configuration and restart Claude Desktop or your MCP client to load the Vectorize server.
Test retrieval with a question
Ask Claude a question that your pipeline documents can answer. The retrieve tool performs hybrid vector + keyword search and returns the most relevant document chunks.
Extract text from a document
To convert a document to Markdown, provide the base64-encoded content and its MIME type. The extract tool handles PDFs, Word docs, Excel files, and more.
Vectorize Examples
Client configuration
Complete claude_desktop_config.json entry for the Vectorize MCP server with all required environment variables.
{
"mcpServers": {
"vectorize": {
"command": "npx",
"args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
"env": {
"VECTORIZE_ORG_ID": "your-org-id",
"VECTORIZE_TOKEN": "your-api-token",
"VECTORIZE_PIPELINE_ID": "your-pipeline-id"
}
}
}
}Prompts to try
Example prompts that exercise retrieve, deep-research, and extract capabilities.
- "Search my documents for information about Q4 revenue projections"
- "Run a deep research on our customer churn data and summarise the key findings"
- "Find the top 5 most relevant passages about GDPR compliance in my pipeline"
- "Extract the text from this PDF and convert it to Markdown so I can review it"Troubleshooting Vectorize
Server fails to start with 'missing environment variable' errors
All three variables โ VECTORIZE_ORG_ID, VECTORIZE_TOKEN, and VECTORIZE_PIPELINE_ID โ must be present in the env block. Copy them exactly from the Vectorize dashboard; they are case-sensitive.
retrieve returns empty results even though the pipeline has documents
Verify the VECTORIZE_PIPELINE_ID matches an active, indexed pipeline. If you recently uploaded documents, the indexing job may still be running. Check pipeline status in the Vectorize dashboard.
deep-research tool is slow to respond
Deep research synthesises answers across many documents and is inherently slower than a single retrieve call. If the pipeline is large, this is expected. Use the k parameter on retrieve for faster, targeted lookups.
Frequently Asked Questions about Vectorize
What is Vectorize?
Vectorize is a Model Context Protocol (MCP) server that โ๏ธ ๐ - [vectorize](https://vectorize.io) mcp server for advanced retrieval, private deep research, anything-to-markdown file extraction and text chunking. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Vectorize?
Install via npm with the command: npx -y @vectorize-io/vectorize-mcp-server. Then add the server configuration to your AI client's JSON config file (e.g., claude_desktop_config.json or .cursor/mcp.json).
Which AI clients work with Vectorize?
Vectorize works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Vectorize free to use?
Yes, Vectorize is open source and available under the MIT License license. You can use it freely in both personal and commercial projects.
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