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Vectorize MCP Server

by vectorize-io

Search Vectorize pipelines, extract files to Markdown, and run private research through MCP.

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Install Vectorize MCP Server

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "vectorize-io-vectorize-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "@vectorize-io/vectorize-mcp-server@latest"
      ],
      "env": {
        "VECTORIZE_ORG_ID": "YOUR_VALUE_HERE",
        "VECTORIZE_TOKEN": "YOUR_VALUE_HERE",
        "VECTORIZE_PIPELINE_ID": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

We couldn’t confirm it starts correctly

We ran the install command but it didn't respond within our test window — this can mean a slow first-time install rather than a real problem.

npx -y @vectorize-io/vectorize-mcp-server@latest

No response to initialize.

This is an experimental automated check and can have false negatives — missing environment variables, a slow cold install, etc. It doesn’t necessarily mean something’s wrong. Last checked 28d ago.

Overview

The vectorize-io/vectorize-mcp-server MCP server connects AI clients to Vectorize for document retrieval, file extraction, text chunking, and private deep research. It runs through npx and uses a Vectorize organization ID, token, and pipeline ID supplied as environment variables. Use it when an agent needs vector search over a configured pipeline, Markdown output from an uploaded file, or a research report that can include web search.

Use cases

  • Search documents stored in a Vectorize pipeline
  • Extract PDF content into Markdown chunks
  • Generate private research reports from pipeline data
  • Let agents retrieve relevant source documents

Key features

  • Vector document retrieval
  • File-to-Markdown extraction
  • Text chunking
  • Private deep research
  • Optional web search for research

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

20/21 checks healthy over the last 79d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 10d ago via OSV.dev · @vectorize-io/vectorize-mcp-server@latest (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars115Stargazers on the source repository.
npm downloads117Package downloads in the last 30 days.
Last commit2d agoMost recent push to the default branch.
Install checkInconclusiveDidn't respond in our test window — often a slow first install.
Directory activity5 viewsConfig copies, upvotes, and views on AllMCPs.

How Vectorize MCP Server works

What vectorize-io/vectorize-mcp-server MCP server does

The vectorize-io/vectorize-mcp-server MCP server exposes selected Vectorize capabilities to MCP-compatible AI clients. It is intended for workflows that need retrieval from a Vectorize pipeline, conversion of a document into Markdown, or private deep research based on pipeline data.

The server does not provide a local search index or document store of its own. Its operations depend on the Vectorize organization, access token, and pipeline configured at startup. That makes it a fit for agents that already have a Vectorize pipeline available and need to invoke it through MCP.

How it works

The process is launched with the npm package through npx. At startup, it reads three environment variables: the Vectorize organization identifier, an authentication token, and the pipeline identifier. An MCP client then exposes the server's operations to an agent.

Retrieval accepts a natural-language question and a requested result count. The server passes that request to Vectorize's retrieval API and returns matching documents. Extraction accepts a Base64-encoded file and its MIME type, then uses Vectorize to produce Markdown-formatted text and chunks. Deep research accepts a query and a setting for web search, producing research from the configured pipeline.

Setup and configuration

Install and run the vectorize-io/vectorize-mcp-server MCP server with:

server.ts
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

The same command and variables can be placed in an MCP client configuration. The repository documents configuration for VS Code, Claude Desktop, Windsurf, Cursor, and Cline. VS Code configuration can be stored in user settings or in a workspace-level .vscode/mcp.json file. The token is represented as a password input in the supplied VS Code setup examples.

Tools and capabilities

The vectorize-io/vectorize-mcp-server MCP server provides three documented operations:

  • retrieve performs vector retrieval using a question and k, the requested number of results. For example, an agent can ask for the financial health of a company and request five results.
  • extract accepts base64document and contentType. It extracts document text and returns it in Markdown chunks, supporting an “any file to Markdown” workflow as described by the project.
  • deep-research accepts query and webSearch. It generates private deep research from the configured pipeline, with the web-search option allowing the request to include web search.

These operations cover separate stages of an agent workflow: finding relevant pipeline content, preparing file content for model use, and generating a research result.

Limitations and notes

It also does not specify retrieval ranking behavior, maximum input sizes, response schemas, pipeline creation steps, or the exact scope of web search.

A valid Vectorize organization ID, token, and pipeline ID are required for the documented startup configuration. The server is configured to use one pipeline ID through the environment, so the material does not establish how an agent selects among multiple pipelines during a session. Client compatibility is documented through configuration examples rather than through a detailed compatibility matrix.

Read the full README View source on GitHub

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Frequently Asked Questions about Vectorize MCP Server

Set VECTORIZE_ORG_ID, VECTORIZE_TOKEN, and VECTORIZE_PIPELINE_ID, then run npx -y @vectorize-io/vectorize-mcp-server@latest.

The documented configuration requires VECTORIZE_ORG_ID, VECTORIZE_TOKEN, and VECTORIZE_PIPELINE_ID.

Yes. The repository includes an MCP configuration example for Claude, along with Windsurf, Cursor, and Cline.

Yes. Its extract operation accepts a Base64-encoded document and content type; the documentation shows application/pdf as an example.

It documents retrieve for vector search, extract for Markdown extraction and chunking, and deep-research for pipeline-based research.

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