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MCP JinaAI Reader Server

by spences10
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mcp-jinaai-reader

⚠️ Notice

This repository is no longer maintained.

The functionality of this tool is now available in mcp-omnisearch, which combines multiple MCP tools in one unified package.

Please use mcp-omnisearch instead.

A Model Context Protocol (MCP) server for integrating Jina.ai's Reader API with LLMs. This server provides efficient and comprehensive web content extraction capabilities, optimized for documentation and web content analysis.

Related MCP server: Jina AI Remote MCP Server

Features

  • 📚 Advanced web content extraction through Jina.ai Reader API

  • 🚀 Fast and efficient content retrieval

  • 📄 Complete text extraction with preserved structure

  • 🔄 Clean format optimized for LLMs

  • 🌐 Support for various content types including documentation

  • 🏗️ Built on the Model Context Protocol

Configuration

This server requires configuration through your MCP client. Here are examples for different environments:

Cline Configuration

Add this to your Cline MCP settings:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "node",
			"args": ["-y", "mcp-jinaai-reader"],
			"env": {
				"JINAAI_API_KEY": "your-jinaai-api-key"
			}
		}
	}
}

Claude Desktop with WSL Configuration

For WSL environments, add this to your Claude Desktop configuration:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-reader"
			]
		}
	}
}

Environment Variables

The server requires the following environment variable:

  • JINAAI_API_KEY: Your Jina.ai API key (required)

API

The server implements a single MCP tool with configurable parameters:

read_url

Convert any URL to LLM-friendly text using Jina.ai Reader.

Parameters:

  • url (string, required): URL to process

  • no_cache (boolean, optional): Bypass cache for fresh results. Defaults to false

  • format (string, optional): Response format ("json" or "stream"). Defaults to "json"

  • timeout (number, optional): Maximum time in seconds to wait for webpage load

  • target_selector (string, optional): CSS selector to focus on specific elements

  • wait_for_selector (string, optional): CSS selector to wait for specific elements

  • remove_selector (string, optional): CSS selector to exclude specific elements

  • with_links_summary (boolean, optional): Gather all links at the end of response

  • with_images_summary (boolean, optional): Gather all images at the end of response

  • with_generated_alt (boolean, optional): Add alt text to images lacking captions

  • with_iframe (boolean, optional): Include iframe content in response

Development

Setup

  1. Clone the repository

  2. Install dependencies:

npm install

  1. Build the project:

npm run build

  1. Run in development mode:

npm run dev

Publishing

  1. Update version in package.json

  2. Build the project:

npm run build

  1. Publish to npm:

npm publish

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see the LICENSE file for details.

Acknowledgments

Available Tools

1 tool
read_urlB

Convert any URL to LLM-friendly text using Jina.ai Reader

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to process
no_cacheNoBypass cache for fresh results
formatNoResponse format (json or stream)json
timeoutNoMaximum time in seconds to wait for webpage load
target_selectorNoCSS selector to focus on specific elements
wait_for_selectorNoCSS selector to wait for specific elements
remove_selectorNoCSS selector to exclude specific elements
with_links_summaryNoGather all links at the end of response
with_images_summaryNoGather all images at the end of response
with_generated_altNoAdd alt text to images lacking captions
with_iframeNoInclude iframe content in response

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic function without disclosing behavioral traits like rate limits, authentication needs, error handling, or performance characteristics. It mentions the external service (Jina.ai Reader) but doesn't explain implications of using a third-party service.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality, making every word earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters and no output schema, the description is insufficient. It doesn't explain what 'LLM-friendly text' means in practice, doesn't describe the response format, and provides no guidance on parameter interactions or error cases. The lack of output schema increases the need for more descriptive context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond the schema, maintaining the baseline score. It doesn't explain relationships between parameters or provide usage examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Convert') and resource ('any URL') while specifying the method ('using Jina.ai Reader') and output format ('LLM-friendly text'). It distinguishes this as a URL-to-text conversion tool with no siblings to differentiate from.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context ('Convert any URL to LLM-friendly text') but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or limitations. With no sibling tools, the baseline is adequate but lacks specific usage scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.4/5.0
Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'read_url' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server with a purpose that could reasonably support more operations, such as handling different URL types or providing metadata extraction. This minimal set feels thin and under-scoped for the domain.

Completeness2/5

The server's domain appears to be URL content reading, but the single tool only covers basic text conversion. There are obvious gaps, such as no tools for handling errors, extracting structured data, or managing different content formats, which limits agent effectiveness.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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