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jina-ai

Jina AI Remote MCP Server

Official
by jina-ai
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TypeScript
Remote

read_url

Extract web page content and convert it to clean, readable markdown format for analysis, bypassing paywalls and obtaining structured text data from websites.

Instructions

Extract and convert web page content to clean, readable markdown format. Perfect for reading articles, documentation, blog posts, or any web content. Use this when you need to analyze text content from websites, bypass paywalls, or get structured data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesThe complete URL of the webpage or PDF file to read and convert (e.g., 'https://example.com/article'). Can be a single URL string or an array of URLs for parallel reading.
withAllLinksNoSet to true to extract and return all hyperlinks found on the page as structured data
withAllImagesNoSet to true to extract and return all images found on the page as structured data

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.0.0
    • addedInput schema / properties / url / anyOf
      Added value: +[
+  {
+    "format": "uri",
+    "type": "string"
+  },
+  {
+    "items": {
+      "format": "uri",
+      "type": "string"
+    },
+    "type": "array"
+  }
+]
    • changedInput schema / properties / url / description
      Previous value: -"The complete URL of the webpage or PDF file to read and convert (e.g., 'https://example.com/article')"New value: +"The complete URL of the webpage or PDF file to read and convert (e.g., 'https://example.com/article'). Can be a single URL string or an array of URLs for parallel reading."
    • removedInput schema / properties / url / format
      Removed value: -"uri"
    • removedInput schema / properties / url / type
      Removed value: -"string"
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the core functionality (extracting and converting content) and mentions bypassing paywalls as a behavioral trait. However, it doesn't address important behavioral aspects like rate limits, authentication requirements, error handling, or what happens with malformed URLs.

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 perfectly front-loaded with the core purpose in the first sentence, followed by usage context. Every sentence earns its place by providing distinct value - the first states what it does, the second gives content examples, and the third provides usage scenarios. No wasted words.

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

Completeness3/5

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

For a tool with 3 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate but incomplete context. It covers the purpose and usage well but lacks behavioral details about rate limits, authentication, error handling, and doesn't describe the output format (though no output schema exists to document this).

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%, so the schema already fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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 verbs ('extract and convert') and resources ('web page content to clean, readable markdown format'). It distinguishes from sibling tools by focusing on content extraction/conversion rather than screenshot capture, PDF extraction, or search operations.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool ('when you need to analyze text content from websites, bypass paywalls, or get structured data') and gives examples of appropriate content types (articles, documentation, blog posts). However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

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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