npm package: @iflow-mcp/jina-ai-mcp-server
Description: MCP server for Jina AI services
Latest version: 1.0.4

# Jina AI MCP Server
[![smithery badge](https://smithery.ai/badge/jina-ai-mcp-server)](https://smithery.ai/server/jina-ai-mcp)
[![smithery badge](https://smithery.ai/badge/jina-ai-mcp-server)](https://smithery.ai/server/jina-ai-mcp-server)

An MCP server that provides access to Jina AI's powerful web services through Claude. This server implements three main tools:

- Web page reading and content extraction
- Web search
- Fact checking/grounding

<a href="https://glama.ai/mcp/servers/c1l6ib2j49"><img width="380" height="200" src="https://glama.ai/mcp/servers/c1l6ib2j49/badge" alt="mcp-jina-ai MCP server" /></a>

## Features

### Tools

#### `read_webpage`
- Extract content from web pages in a format optimized for LLMs
- Supports multiple output formats (Default, Markdown, HTML, Text, Screenshot, Pageshot)
- Options for including links and images
- Ability to generate alt text for images
- Cache control options

#### `search_web`
- Search the web using Jina AI's search API
- Configurable number of results (default: 5)
- Support for image retention and alt text generation
- Multiple return formats (markdown, text, html)
- Returns structured results with titles, descriptions, and content

#### `fact_check`
- Fact-check statements using Jina AI's grounding engine
- Provides factuality scores and supporting evidence 
- Optional deep-dive mode for more thorough analysis
- Returns references with key quotes and supportive/contradictory classification

## Setup

### Prerequisites

You'll need a Jina AI API key to use this server. Get one for free at https://jina.ai/

### Installation

There are two ways to use this server:

#### Installing via Smithery

To install Jina AI for Claude Desktop automatically via [Smithery](https://smithery.ai/server/jina-ai-mcp-server):

```bash
npx -y @smithery/cli install jina-ai-mcp-server --client claude
```

#### Option 1: NPX (Recommended)
Add this configuration to your Claude Desktop config file:

```json
{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "npx",
      "args": [
        "-y",
        "jina-ai-mcp-server"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}
```

#### Option 2: Local Installation
1. Clone the repository
2. Install dependencies:
```bash
npm install
```

3. Build the server:
```bash
npm run build
```

4. Add this configuration to your Claude Desktop config:
```json
{
  "mcpServers": {
    "jina-ai-mcp-server": {
      "command": "node",
      "args": [
        "/path/to/jina-ai-mcp-server/dist/index.js"
      ],
      "env": {
        "JINA_API_KEY": "<YOUR_KEY>"
      }
    }
  }
}
```

### Config File Location

On MacOS:
```bash
~/Library/Application Support/Claude/claude_desktop_config.json
```

On Windows:
```bash
%APPDATA%/Claude/claude_desktop_config.json
```

### Debugging

Since MCP servers communicate over stdio, debugging can be challenging. We recommend using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector):

```bash
npm run inspector
```

The Inspector will provide a URL to access debugging tools in your browser.

## API Response Types

All tools return structured JSON responses that include:

- Status codes and metadata
- Formatted content based on the requested output type
- Usage information (token counts)
- When applicable: images, links, and additional metadata

For detailed schema information, see `schemas.ts`.


## Running evals

The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found [here](https://www.mcpevals.io/docs).

```bash
OPENAI_API_KEY=your-key  npx mcp-eval evals.ts index.ts
```