What is Vectorize MCP Server

The Vectorize MCP Server is an implementation of the Model Context Protocol that integrates with Vectorize, enabling advanced vector retrieval and text extraction functionalities. It allows users to efficiently manage documents and perform various operations such as retrieval, text extraction, and deep research using Vectorize’s capabilities.

Use cases

The server’s use cases include retrieving documents based on vector searches, extracting text from various document formats into Markdown, and conducting deep research queries to generate detailed reports or summaries. These functionalities are beneficial for organizations needing to handle large volumes of data, automate workflows, or enhance data accessibility.

How to use

Users can run the Vectorize MCP Server using ‘npx’ with their specific Vectorize credentials, such as Organization ID, Token, and Pipeline ID. For easier access, it can also be integrated into VS Code, where users can install it with one-click buttons or by adding JSON configuration to their settings. Additionally, manual configuration options are provided for those who prefer customization.

Key features

Key features include the ability to perform vector searches to retrieve documents, extract and chunk text from documents into Markdown format, and generate comprehensive deep research reports. The server supports various document formats and integrates seamlessly with Vectorize’s API, enabling users to leverage powerful data processing tools.

Where to use

The Vectorize MCP Server can be used in environments that require advanced document processing, such as research institutions, corporate settings for data analysis, and software development scenarios where efficient document retrieval and management are vital. It is suitable for professionals dealing with large datasets, requiring quick access to information and insights.

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

Contributing

1. Fork the repository
2. Create your feature branch
3. Submit a pull request