Jina AI MCP Server (Node.js Version)

An MCP server for Jina AI, providing tools for embeddings, reranking, and generation. This is the Node.js version.

Available Tools

This server provides the following tools, which are direct interfaces to the Jina AI Search Foundation APIs:

• `embeddings`: Creates an embedding vector representing the input text.
• `rerank`: Reranks a list of documents based on a query.
• `read`: Extracts clean, LLM-friendly content from a single website URL.
• `search`: Performs a web search and returns LLM-friendly results.
• `deepsearch`: Combines web searching, reading, and reasoning for comprehensive investigation.
• `segment`: Splits text into semantic chunks or counts tokens.
• `classify`: Performs zero-shot classification for text.
• `get_help`: Returns the full Jina AI API documentation used to build this server.

Connecting with MCP Clients

To connect this server to your MCP-compatible client (like Cursor, shell-ai, etc.), you first need to publish this package to NPM or install it from a local path.

Using with `npx` (After Publishing)

Once the package is published on NPM, you can configure your client to use it with `npx`. Create a `.env` file with your `JINA_API_KEY` in the directory where you run the client, or make sure the environment variable is set.

Example for `mcpServers.json`:

{
  "jina-ai-server": {
    "command": "npx",
    "args": [
      "jina-ai-mcp-server-nodejs"
    ],
    "env": {
      "JINA_API_KEY": "your_jina_api_key_here"
    }
  }
}

Note: Passing the API key via `env` in the configuration is more secure than a global environment variable.

Local Development

1. Clone the repository.

2. Install dependencies:

npm install

3. Create a `.env` file in the root of the project and add your Jina AI API key.

echo "JINA_API_KEY=your_jina_ai_api_key_here" > .env

4. Run the server in development mode:

npm run dev

Docker

Building for Production

To compile the TypeScript code to JavaScript:

npm run build

The compiled output will be in the `dist` directory.

You can then run the compiled code with:

npm start