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Tollmint

by com.tollmint

web_fetch

Fetch a URL and return the main content as clean markdown, with navigation, cookie banners, footers, and related-post chrome removed. Extracts title, byline, publication date, and excerpt. Content detection is driven by link density rather than class names, so it survives CSS refactors. Reports how much of the page was retained and whether it had to fall back to the full body, so you can tell a clean extraction from a guess. Honours robots.txt; refuses private addresses and pages behind authentication. Costs $0.003000 per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNoPage to analyse. Fetched respecting robots.txt.
htmlNoRaw HTML, if you already have it. Takes precedence over url.
max_wordsNoTruncate the markdown at this many words. Truncation is reported.
include_linksNoKeep inline links as markdown. Disable to cut tokens when only prose matters.

TDQS

A4.3/5.0
Behavior5/5

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

Discloses extraction method (link density), fallback behavior, what content is removed (navigation, banners, etc.), and reports retention stats. Also mentions honoring robots.txt and refusing private/auth pages. No annotations present, so description fully carries transparency burden.

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?

Three well-organized sentences, front-loaded with core purpose. Each sentence adds unique value (purpose, detection method, restrictions/cost). Slightly long but efficient; no wasted words.

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?

Covers input parameters, output contents, extraction approach, restrictions, and pricing. Lacks return format details or error states, but given no output schema, the description provides adequate context for a content-extraction tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds context beyond schema: url's robots.txt compliance, max_words truncation reporting, include_links token reduction hint. Schema already provides descriptions for all 4 parameters, but description enriches usage semantics for each.

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 clearly states the tool 'Fetch a URL and return the main content as clean markdown' with specific verb and resource. It distinguishes from sibling tools (mostly psych/design analysis) by focusing on web content extraction and cleanup.

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 for extracting clean content from web pages but does not explicitly state when to use or provide comparisons to alternatives. Mentions restrictions (robots.txt, private addresses) but lacks explicit guidance on context or exclusions.

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

A4/5.0
Disambiguation5/5

All 20 tools have clearly distinct purposes, grouped by domain prefixes (content_ai, design_ai, legal, psych, ref, tollmint, web). Even similar-sounding tools like psych_dark-pattern-detect and psych_dark-patterns are differentiated as a live scanner versus a reference taxonomy. No two tools overlap in functionality.

Naming Consistency4/5

Naming mostly follows a prefix_descriptive pattern, but some tools use hyphens (content_ai-score, design_ai-slop-detect) while others use underscores (legal_accessibility, psych_biases). This minor inconsistency prevents a perfect score, but the pattern is still clear and readable.

Tool Count4/5

20 tools is slightly above the typical 'well-scoped' range, but each tool serves a specific, justifiable need across multiple domains (legal, psychology, content analysis, geocoding, internal). The count feels comprehensive rather than bloated.

Completeness5/5

The tool surface covers all major areas implied by the domain prefixes: comprehensive legal compliance references, extensive psychology/behavioral design tools, content and design analysis, geocoding, and internal server management. No obvious gaps for the intended use cases.

Resources