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Read-onlyIdempotent

Fetch a web page and return clean GitHub-flavored markdown at a strict token budget. DOM-density scoring strips boilerplate (CSS, JS, navbars, tracking) — raw HTML averages 80-95% noise. Requires a valid API key (Bearer token); billing is $0.0005 flat per read — insufficient balance returns HTTP 402. Fetch failures return HTTP 422 and are refunded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesURL of the web page to fetch and convert to markdown.
max_tokensNoOptional token budget — markdown is truncated to this many estimated tokens. Omit for full page.
include_linksNoIf true, hyperlinks are kept as markdown links.
preserve_tablesNoIf true, HTML tables are preserved as GFM tables.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond those: token-budget truncation, DOM-density boilerplate stripping, API key requirements, flat pricing per read, HTTP 402 for insufficient balance, and HTTP 422 with refund for fetch failures. No contradiction with annotations.

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?

Four sentences, each earning its place: purpose, processing behavior, auth/billing, and failure handling. The core purpose is front-loaded, and no padding or redundant restatement of the schema exists.

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?

For a straightforward read-oriented tool with no output schema, the description covers what the tool does, how it processes input, what credentials are needed, what it costs, and what errors to expect. Nothing essential is missing for an agent to invoke 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 the parameters are already fully documented. The description only adds marginal context around max_tokens via 'strict token budget' and 'estimated tokens' but does not meaningfully enrich the meaning of url, include_links, or preserve_tables beyond what the schema provides.

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 opens with a specific verb and resource: 'Fetch a web page and return clean GitHub-flavored markdown.' This unambiguously distinguishes the tool from siblings like convert or generate, even without naming them. The title 'Web Page Reader' reinforces the purpose.

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?

The description gives clear context for when to use this tool: whenever a web page needs to be fetched and converted to markdown. It does not explicitly mention when not to use it or name an alternative, but the use case is communicated clearly enough for an agent to select it appropriately.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool performs a distinct operation: conversion, diffing, patching, pruning, llms.txt generation, validation, and cost estimation. The descriptions explicitly cross-reference related tools (diff/patch, generate/validate), which further prevents misselection.

Naming Consistency5/5

All seven tools use lowercase single-word imperative verbs (convert, diff, estimate, generate, patch, prune, validate), forming a consistent and predictable naming convention.

Tool Count5/5

Seven tools is within the ideal range and every tool has a clear purpose. The set is compact rather than sprawling, with no redundant or filler tools.

Completeness4/5

The paired workflows are complete: diff/patch, generate/validate, and convert/prune cover their stated operations without dead ends. The main gap is that the estimate workflow points to an external pricing endpoint for supported models rather than exposing model/pricing lookup as a first-class tool, which is a minor obstacle.

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