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IA-QA — 152 QA & Dev Tools for AI Agents

by io.github.JcJamet
OverviewDiscussionsAdmin

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

NameRequiredDescriptionDefault
inputYesThe text to analyse

Output Schema

NameRequiredDescriptionDefault
charsNo
linesNo
wordsNo
sentencesNo
paragraphsNo
chars_no_spaceNo
reading_time_minutesNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "input": "One two three.\nFour five!\n\nSix?"
+  }
+]
  2. Changed1 schema field changed
    • addedOutput schema / properties
      Added value: +{
+  "chars": {},
+  "chars_no_space": {},
+  "lines": {},
+  "paragraphs": {},
+  "reading_time_minutes": {},
+  "sentences": {},
+  "words": {}
+}
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "additionalProperties": true,
+  "type": "object"
+}
  4. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/idempotentHint/non-destructive/openWorld=false, so safety is covered. The description still adds genuine behavioral detail beyond them: sentence counting is abbreviation-aware (titles, acronyms, initials, decimals, URLs, emails do not terminate a sentence) and unpunctuated text counts as one sentence, plus reading time is an estimate in minutes. That is exactly the kind of non-obvious counting behavior an agent cannot infer from annotations or schema.

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?

Two sentences, front-loaded with the metric inventory, then edge cases, then use cases — a clean most-important-first ordering. Every clause carries information; nothing repeats the name or the schema.

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?

An output schema exists, so return values needn't be described, and annotations cover the safety profile for a pure read. With one required string parameter, the description supplies the remaining unknowns: what is measured, how the trickiest metric (sentences) behaves at the boundaries, and why an agent would call it.

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?

The single parameter has 100% schema description coverage ('The text to analyse'), so the schema carries the load and baseline 3 applies. The description adds only the framing 'any text' and implicit edge-case handling (URLs, emails, missing punctuation) — useful but not syntax or format guidance beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

A specific verb (compute) plus resource (text statistics), with the exact metric set enumerated: character count with/without spaces, word, line, sentence, paragraph, and reading time. It is unambiguous what is returned. It never names a sibling it might be confused with (count_code_lines is the nearest overlap), so it stops short of a 5.

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 final sentence gives three concrete usage contexts — validating form field lengths, evaluating LLM output verbosity, content auditing — which tells an agent when this tool is the right pick. There are no explicit exclusions or named alternatives, so it is clear context without deliberately routing away from siblings.

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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