IA-QA — 130+ QA & Dev Tools for AI Agents

text_stats

Read-onlyIdempotent

Compute comprehensive statistics for any text: character count (with and without spaces), word count, line count, sentence count, paragraph count, and estimated reading time in minutes. Sentence counting is abbreviation-aware — titles (Mr., Dr.), acronyms (U.S., i.e., p.m.), initials, decimals, URLs and emails do not end a sentence, and a text with no terminal punctuation still counts as one. Use for validating form field lengths, evaluating LLM output verbosity, or content auditing.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`input`	Yes	The text to analyse

Output Schema

TableJSON Schema

Name	Required	Description	Default
`chars`	No
`lines`	No
`words`	No
`sentences`	No
`paragraphs`	No
`chars_no_space`	No
`reading_time_minutes`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedJun 21, 2026

addedOutput schema / properties

Added value: +{
+  "chars": {},
+  "chars_no_space": {},
+  "lines": {},
+  "paragraphs": {},
+  "reading_time_minutes": {},
+  "sentences": {},
+  "words": {}
+}

Changed1 schema field changedJun 10, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}

First observedMay 26, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which already convey safety. The description adds valuable behavioral context beyond annotations by detailing abbreviation-aware sentence counting, handling of titles, acronyms, URLs, etc., and the edge case of text without terminal punctuation. This enriches the agent's understanding of the tool's behavior.

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?

The description is concise, with each sentence adding unique value: the first lists metrics, the second explains sentence detection nuances, and the third lists use cases. No fluff, well-structured for quick reading.

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 simple tool with one parameter, full schema coverage, and detailed behavioral transparency on edge cases, the description is complete. It covers what statistics are returned, how edge cases are handled, and use cases, which fully equips 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% with one parameter 'input' described as 'The text to analyse', which fully documents the parameter. The description adds context on what statistics are computed but doesn't need to add more parameter detail. Baseline 3 is appropriate since schema covers parameter meaning fully.

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 uses a specific verb ('Compute') and clearly identifies the resource (text statistics) and enumerates exact metrics (character count, word count, line count, etc.). It distinguishes itself from siblings by detailing the comprehensive set of statistics and the reading time estimate, which is specific to this tool.

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 explicitly lists use cases such as validating form field lengths, evaluating LLM output verbosity, and content auditing, providing clear context for when to use this tool. It does not explicitly name alternative tools or state when not to use it, but the uniqueness of the tool's purpose minimizes ambiguity.

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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