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by kulas-100
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

web_reader

Fetch a public webpage and convert it into clean, structured Markdown by stripping headers, footers, ads, and navigation, so LLMs can read articles, docs, and blog posts with fewer tokens.

Instructions

Fetch a public webpage URL and extract clean, structured Markdown optimized for LLM token efficiency, stripping headers, footers, ads, and navigation menus. Use when reading articles, blog posts, documentation, or public web pages. Read-only and idempotent.

Input Schema

NameRequiredDescriptionDefault
urlYesThe absolute HTTP or HTTPS URL of the webpage to fetch and convert to Markdown.
max_lengthNoMaximum character limit for the returned Markdown content (1,000 to 50,000, default 15000).
include_linksNoWhether to extract and append a catalog of all hyperlinks found in the document.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.3
    • changedInput schema / properties / include_links / description
      Previous value: -"Whether to return page links catalog"New value: +"Whether to extract and append a catalog of all hyperlinks found in the document."
    • changedInput schema / properties / max_length / description
      Previous value: -"Max markdown length (default 15000)"New value: +"Maximum character limit for the returned Markdown content (1,000 to 50,000, default 15000)."
    • changedInput schema / properties / url / description
      Previous value: -"The full URL to fetch"New value: +"The absolute HTTP or HTTPS URL of the webpage to fetch and convert to Markdown."
  2. First observedv0.1.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations supplied, the description does the heavy lifting: it discloses the read-only, idempotent safety profile and explains what the tool strips (headers, footers, ads, navigation menus) and what it returns (Markdown for token efficiency). It omits failure behavior — blocked, paywalled, or JavaScript-rendered pages — which an agent calling a scraper would benefit from knowing.

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 with zero padding; the core action and transformation lead, and the usage guidance plus safety profile follow compactly.

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 three-parameter fetch tool with no output schema and no annotations, the description covers action, scope, output shape, and safety. The remaining gap is error/edge-case behavior, which is not covered anywhere else in the structured data.

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 all three parameters are already documented in the schema with defaults and ranges. The description adds only indirect context ('LLM token efficiency') that motivates the length limit, without further clarifying parameter behavior, 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 ('fetch') and resource (a public webpage URL) plus the transformation performed ('extract clean, structured Markdown'), which cleanly separates it from the sibling web_search tool that locates pages rather than reading them.

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 a clear usage context ('reading articles, blog posts, documentation, or public web pages') and implicitly scopes out non-public/authenticated pages via 'public webpage URL'. It does not name an explicit alternative or state when not to use it, so it stops short of full routing guidance.

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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