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Read URL as Markdown

read_url

Fetch any web page and get clean, LLM-ready Markdown with full content, automatic JS rendering, and honest errors when access is blocked.

Instructions

Fetch a web page and return its content as clean, LLM-ready Markdown. Full content by default (no truncation). Handles HTML, plain text, Markdown, and JSON responses. JavaScript-heavy pages are rendered automatically in a real browser when the static HTML is an empty SPA shell. Returns an honest typed error when the site blocks access or the content cannot be read — it never silently returns partial or fake content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNoIf true, return the raw page HTML instead of extracted Markdown. Default: false.
urlYesThe URL to fetch (http or https).
renderNoJS rendering in a real browser: true = always render, false = never render. Default: automatic — renders only when the static HTML turns out to be an empty SPA shell.
headersNoExtra HTTP headers to send, e.g. an Authorization bearer token or a Cookie for pages behind a login. Example: {"Authorization": "Bearer ...", "Cookie": "session=..."}.
max_lengthNoMaximum number of characters to return. Default 0 = unlimited (full page). If output is truncated, the response says so and tells you how to continue.
start_indexNoCharacter offset to start from, for paging through a long page. Default: 0.
Behavior4/5

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 explicitly states that content is not truncated by default, formats served, automatic JS rendering behavior, and that it returns an honest typed error rather than silently returning partial or fake content. This is highly transparent, though it omits some details like rate limits or redirect handling.

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 compact and front-loaded with the primary purpose, followed by key behavioral details. Every sentence earns its place, covering formats, rendering, and error handling without unnecessary fluff. Structurally ideal for quick parsing.

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?

Given the tool's complexity (6 parameters, nested objects, no output schema or annotations), the description covers the essential behavioral aspects: return format, default truncation behavior, handled content types, automatic rendering, and error honesty. The schema handles parameter details, and the description provides sufficient operational context, leaving only minor gaps like timeouts or redirects.

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 covers 100% of parameters with detailed descriptions, so the baseline is 3. The description adds some context (e.g., 'Full content by default (no truncation)' aligns with max_length, and SPA shell behavior relates to render), but it does not provide substantial meaning beyond what the schema already offers.

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

Purpose4/5

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

The description clearly states the tool fetches a web page and returns its content as clean, LLM-ready Markdown, which is a specific verb and resource. However, it does not explicitly differentiate from the sibling tool 'read_docs', so it misses the highest mark for sibling distinction.

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 contexts (e.g., handling HTML, plain text, Markdown, JSON, and automatic JS rendering for SPAs) but provides no explicit guidance on when to use this tool versus 'read_docs' or any exclusions. It gives clear context but no direct alternative comparison.

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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