Vectorize Mcp Server

What is Vectorize MCP Server?

The ### Vectorize MCP Server is an open-source project designed to facilitate the management and processing of data within the Vectorize ecosystem. It serves as a backend server that handles various tasks related to data manipulation, storage, and retrieval, making it an essential component for developers working with Vectorize technologies.

Features of Vectorize MCP Server

• Data Management: Efficiently manage and process large datasets with ease.
• API Integration: Seamlessly integrate with other services through a robust API, allowing for flexible data handling.
• Open Source: Being an open-source project, it encourages community contributions and transparency.
• Scalability: Designed to scale with your needs, whether you're working on small projects or large enterprise solutions.
• Documentation: Comprehensive documentation is available to assist developers in getting started and utilizing the server effectively.

How to Use Vectorize MCP Server

1. Installation: Begin by cloning the repository from GitHub and following the installation instructions provided in the documentation.
2. Configuration: Configure the server settings according to your project requirements. This may include setting up database connections and API keys.
3. API Usage: Utilize the provided API endpoints to interact with the server. You can perform operations such as data retrieval, updates, and deletions.
4. Testing: Conduct thorough testing to ensure that the server operates as expected within your application.
5. Deployment: Once everything is set up and tested, deploy the server to your production environment.

Frequently Asked Questions

Q: Is Vectorize MCP Server free to use?

A: Yes, it is an open-source project and is free to use under the MIT license.

Q: Can I contribute to the Vectorize MCP Server?

A: Absolutely! Contributions are welcome. You can submit issues, feature requests, or pull requests on the GitHub repository.

Q: Where can I find the documentation?

A: The documentation is available at docs.vectorize.io/api/api-mcp-server.

Q: What programming languages are supported?

A: The server is primarily built using languages that are compatible with the Vectorize ecosystem, but it can be integrated with any language that can make HTTP requests.

Q: How do I report a bug?

A: You can report bugs by opening an issue on the GitHub repository, providing as much detail as possible to help the developers address the problem.

Vectorize MCP Server

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>

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