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Tollmint

by com.tollmint

content_ai-score

Measure how mechanically a piece of text reads across 19 stylistic signals: sentence-length variance (burstiness), em-dash rate, negative parallelism, copula avoidance, the characteristic AI vocabulary cluster, hedged superlatives, significance inflation, puffery, vague attribution, over-signposting, formatting tics, and more. Returns a per-sentence breakdown naming which signal each one tripped. Deterministic and model-free, so before-and-after comparisons across an edit are meaningful. NOT an AI detector — it reports stylistic properties, never authorship, and returns no verdict or probability. Authorship classifiers are unreliable and disproportionately misjudge non-native English writers. Costs $0.004000 per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
include_sentencesNo

TDQS

A3.9/5.0
Behavior4/5

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

No annotations exist, so the description carries the full burden. It discloses deterministic behavior, model-free nature, cost per call, and the output structure (per-sentence breakdown). It does not discuss auth or rate limits, but the provided details are extensive.

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?

The description is lengthy but every sentence adds value, front-loading the main purpose. It could be slightly more concise, but it remains efficient given the number of signals listed.

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

Completeness3/5

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

With 2 parameters, no output schema, and no annotations, the description provides a good overview but lacks parameter documentation. The per-sentence breakdown is mentioned but the exact output format is not specified.

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

Parameters2/5

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

Schema description coverage is 0%, and the description does not describe the parameters 'text' or 'include_sentences' beyond the tool's purpose. The default value of 'include_sentences' is not mentioned, and maxLength/minLength for 'text' are not explained.

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 it measures mechanicalness across 19 stylistic signals, provides a per-sentence breakdown, and explicitly distinguishes itself from sibling tools like 'design_ai-slop-detect' by clarifying it is not an AI detector.

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 explains it is deterministic and model-free, making before-and-after comparisons meaningful, and explicitly states it is not an AI detector. However, it does not directly guide when to use this tool versus its siblings beyond the 'not an AI detector' distinction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Tollmint toolsInspect
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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