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Web Read - URL to clean Markdown or text for LLMs

web_read
Read-onlyIdempotent

Fetch any public web page and return its main content as clean Markdown (or plain text) with title, author, date and language. Boilerplate (menus, cookie banners, footers) removed. Respects robots.txt. ~1-3 s. Free: 25 calls/day per IP. Paid: $0.002 per call via x402 at https://localhost/x402/web-read.

Input Schema

NameRequiredDescriptionDefault
urlNoPage URL (https://...)
formatNomarkdown or textmarkdown
maxCharsNoCut the content after this many characters

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnly, openWorld and idempotent, so the safety profile is covered; the description goes well beyond by disclosing that boilerplate is stripped, that robots.txt is respected, the ~1-3 s latency, the 25 calls/day free ceiling per IP, and the $0.002 paid path with its x402 endpoint. Rate limits and cost are exactly the operational facts an agent needs before retrying or looping.

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?

Five short sentences, front-loaded with what the tool returns before operational constraints. Every clause carries distinct information — output shape, cleanup behavior, compliance, latency, cost — with no restatement of the title or name.

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?

There is no output schema, yet the description enumerates the fields an agent will receive (main content, title, author, date, language) and the cleanup that has been applied, so return values are predictable. Combined with latency, quota and pricing, a caller has everything needed to invoke and budget calls 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 the schema already documents url, format (with enum and default) and maxChars (with bounds and default). The description restates the Markdown/plain-text choice but adds nothing about truncation behavior or how maxChars interacts with the returned metadata. Baseline 3 is appropriate when the schema carries the parameter burden.

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 any public web page") plus the transformation applied ("return its main content as clean Markdown (or plain text)") and the metadata returned (title, author, date, language). No sibling tool in the list performs web retrieval, so the tool is unambiguous on its own terms.

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 context for use — any public page, with robots.txt compliance and a stated latency window — which tells an agent this is a general-purpose retrieval tool rather than a site-specific one. It stops short of explicit exclusions (paywalled, JS-rendered, authenticated pages) and names no alternative tool, so it is clear but not fully routing-grade.

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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