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Vectorize集成

STDIO

向量检索和文本提取的MCP服务器

Vectorize MCP Server

A Model Context Protocol (MCP) server implementation that integrates with Vectorize for advanced Vector retrieval and text extraction.

Vectorize MCP server

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:

Install with NPX in VS Code Install with NPX in VS Code Insiders

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

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