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How easy a text is to read

readability
Read-onlyIdempotent

How easy a text is to read, from the readability tool on a-to-mind.com/word-count/ that Verse also runs on its own page descriptions: the Flesch reading ease and Flesch-Kincaid grade with who reads it comfortably, the words and sentences counted, and what a proofread would fix (doubled words, extra spaces, common misspellings, sentences without a capital). No model.

Input Schema

NameRequiredDescriptionDefault
textYesThe text (5 words to 100,000 characters).
response_typeNoWhat to get back: 'text' (default) or 'raw_data' (the structured result as JSON). 'image' is not offered yet.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context beyond annotations: it lists the specific proofreading checks performed and states 'No model,' indicating deterministic computation rather than LLM inference. It stops short of mentioning rate limits or return format details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sprawling sentence with multiple clauses, which makes it dense rather than front-loaded and easy to scan. The phrase 'that Verse also runs on its own page descriptions' is irrelevant to an agent's decision and does not earn its place.

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 simple two-parameter tool with no output schema, the description explains what the result contains (readability scores, counts, proofreading fixes). Annotations cover the safety profile, and the schema covers parameter details. The only minor gap is that response_type behavior is left entirely to the schema.

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 both parameters (text and response_type) are fully documented in the schema. The description does not add syntax or format details beyond what the schema already provides, making the baseline of 3 appropriate.

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 states a specific function: computes readability metrics (Flesch reading ease, Flesch-Kincaid grade), word and sentence counts, and proofreading checks like doubled words and misspellings. This is clearly distinguishable from siblings like skill_words or seo_audit, and an agent can tell exactly what the tool produces without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as skill_words or seo_audit, nor any exclusions or prerequisites. The mention that 'Verse also runs on its own page descriptions' is context about the source, not usage guidance for the agent.

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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