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# Vectorize MCP Server
A Model Context Protocol (MCP) server implementation that integrates with [Vectorize](https://vectorize.io/) 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
```bash
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:
[](https://insiders.vscode.dev/redirect/mcp/install?name=vectorize&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40vectorize-io%2Fvectorize-mcp-server%40latest%22%5D%2C%22env%22%3A%7B%22VECTORIZE_ORG_ID%22%3A%22%24%7Binput%3Aorg_id%7D%22%2C%22VECTORIZE_TOKEN%22%3A%22%24%7Binput%3Atoken%7D%22%2C%22VECTORIZE_PIPELINE_ID%22%3A%22%24%7Binput%3Apipeline_id%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22org_id%22%2C%22description%22%3A%22Vectorize+Organization+ID%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22token%22%2C%22description%22%3A%22Vectorize+Token%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22pipeline_id%22%2C%22description%22%3A%22Vectorize+Pipeline+ID%22%7D%5D) [](https://insiders.vscode.dev/redirect/mcp/install?name=vectorize&config=%7B%22command%22%3A%22npx%22%2C%22args%22%3A%5B%22-y%22%2C%22%40vectorize-io%2Fvectorize-mcp-server%40latest%22%5D%2C%22env%22%3A%7B%22VECTORIZE_ORG_ID%22%3A%22%24%7Binput%3Aorg_id%7D%22%2C%22VECTORIZE_TOKEN%22%3A%22%24%7Binput%3Atoken%7D%22%2C%22VECTORIZE_PIPELINE_ID%22%3A%22%24%7Binput%3Apipeline_id%7D%22%7D%7D&inputs=%5B%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22org_id%22%2C%22description%22%3A%22Vectorize+Organization+ID%22%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22token%22%2C%22description%22%3A%22Vectorize+Token%22%2C%22password%22%3Atrue%7D%2C%7B%22type%22%3A%22promptString%22%2C%22id%22%3A%22pipeline_id%22%2C%22description%22%3A%22Vectorize+Pipeline+ID%22%7D%5D&quality=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)`.
```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": "${input:pipeline_id}"
}
}
}
}
}
```
Optionally, you can add the following to a file called `.vscode/mcp.json` in your workspace to share the configuration with others:
```json
{
"inputs": [
{
"type": "promptString",
"id": "org_id",
"description": "Vectorize Organization ID"
},
{
"type": "promptString",
"id": "token",
"description": "Vectorize Token",
"password": true
},
{
"type": "promptString",
"id": "pipeline_id",
"description": "Vectorize Pipeline ID"
}
],
"servers": {
"vectorize": {
"command": "npx",
"args": ["-y", "@vectorize-io/vectorize-mcp-server@latest"],
"env": {
"VECTORIZE_ORG_ID": "${input:org_id}",
"VECTORIZE_TOKEN": "${input:token}",
"VECTORIZE_PIPELINE_ID": "${input:pipeline_id}"
}
}
}
}
```
## Configuration on Claude/Windsurf/Cursor/Cline
```json
{
"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](https://docs.vectorize.io/api/api-pipelines/api-retrieval)):
```json
{
"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](https://docs.vectorize.io/api/api-extraction)):
```json
{
"name": "extract",
"arguments": {
"base64document": "base64-encoded-document",
"contentType": "application/pdf"
}
}
```
### Deep Research
Generate a Private Deep Research from your pipeline (see official [API](https://docs.vectorize.io/api/api-pipelines/api-deep-research)):
```json
{
"name": "deep-research",
"arguments": {
"query": "Generate a financial status report about the company",
"webSearch": true
}
}
```
## Development
```bash
npm install
npm run dev
```
### Contributing
1. Fork the repository
2. Create your feature branch
3. Submit a pull request
