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read_url

Read any URL and return clean, agent-ready Markdown. Strips HTML noise and preserves semantic content for AI processing.

Instructions

Read any URL and return clean, agent-ready Markdown. Strips HTML noise, preserves semantic content, and returns content optimized for AI consumption. Use this when you need to extract content from a website for an AI agent to process.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "url": "<url>"
    },
    "name": "read_url"
  }
}

Input Schema

Name	Required	Description	Default
`url`	Yes	The URL to read. Must be a publicly accessible HTTP or HTTPS URL.
`fresh`	No	If true, bypass cache and fetch fresh content. Default false.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.5.0Aug 5, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that HTML noise is stripped, semantic content is preserved, and output is optimized for AI consumption. It implies read-only behavior and describes the transformation, adding useful context beyond the schema.

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?

Two sentences, front-loaded with the action and purpose, then efficient elaboration on behavior and intended use. No wasted words.

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

Completeness4/5

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

For a simple tool with two parameters and no output schema, the description provides enough context: what it does, how it transforms content, and when to use it. It does not mention caching behavior (though the 'fresh' parameter hints at it), but overall it is sufficient.

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?

The schema description coverage is 100%, so the parameters (url and fresh) are already well-documented. The description does not add parameter-specific details beyond the schema, so a baseline of 3 is appropriate.

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 uses a specific verb ('Read') and resource ('any URL') and clearly states the output format (Markdown). It distinguishes itself from sibling tools like score_url, map_site, batch, and extract_data by focusing on content extraction and conversion, not scoring, mapping, batching, or data extraction.

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?

It explicitly states when to use: 'when you need to extract content from a website for an AI agent to process.' This provides clear context, though it does not mention alternatives or exclusions, which would make it more complete.

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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