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Read a web page as Markdown

read_url
Read-only

Read a web page for an AI: fetch a public page and return its main content as clean, LLM-ready Markdown (navigation, footers, scripts and cookie banners removed) plus metadata: title, description, author, published/modified dates, canonical URL, language and word count. Reads server-rendered HTML (no JavaScript execution) and respects robots.txt.

Input Schema

NameRequiredDescriptionDefault
urlYesPublic page URL, e.g. "https://example.com/blog/post".
max_charsNoMaximum Markdown length to return.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the readOnlyHint/openWorldHint annotations by disclosing what gets stripped (navigation, footers, scripts, cookie banners), what metadata is returned, the no-JavaScript limitation, and robots.txt compliance. These are exactly the behavioral traits an agent needs to predict output quality and failure modes.

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

Conciseness4/5

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

Front-loaded with the core action and output format, and the trailing sentence carries the two key operational caveats. The metadata enumeration is slightly list-heavy but every item is informative, so waste is minimal.

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

Completeness5/5

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

With no output schema, the description carries the return-value burden and does so fully: it names the Markdown payload, the stripped elements and the metadata fields. Combined with the stated limitations, an agent has everything needed to call it correctly.

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 both 'url' and 'max_chars' are already documented with types, bounds and defaults. The description adds no syntax, format or truncation detail beyond that, so the baseline 3 applies.

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?

States a specific verb and resource ('fetch a public page and return its main content as clean, LLM-ready Markdown') and immediately names the transformation and scope. No sibling tool competes for this job, and the title plus first clause make the function unambiguous.

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?

Gives clear context for use and an implicit exclusion: 'Reads server-rendered HTML (no JavaScript execution)' tells the agent this will fail on JS-rendered SPAs, and 'respects robots.txt' signals blocked pages. It does not name an alternative tool or state explicitly when not to use it, so it falls short of a 5.

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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