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aparajithn

agent-utils-mcp

by aparajithn

tool_text_stats

Calculate word count, character count, sentence count, and estimated reading time for any provided text.

Instructions

Compute text statistics: word count, char count, sentences, reading time.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It clearly identifies a stateless read-only computation (calculating statistics), which does not imply side effects or destructive behavior. However, it does not disclose assumptions such as reading-speed calculations, how sentences are detected, or handling of edge cases like empty or very long text.

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 a single, focused sentence that front-loads the action and then lists the concrete outputs. Every word earns its place, and there is no redundant or vague filler.

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?

This is a simple tool with one parameter and an output schema, so the description does not need to explain return values. The listing of computed metrics is sufficient for a basic utility. Minor missing context around statistical assumptions prevents a perfect score.

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%, so the description needed to compensate, but it adds no direct explanation of the 'text' parameter beyond restating that text is the input. The schema already shows 'text' as a string, so the description provides no additional semantics such as length limits, language assumptions, or formatting requirements.

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 a specific resource ('text statistics'), and it enumerates the exact metrics produced: word count, char count, sentences, reading time. This clearly distinguishes the tool from all sibling utilities like diff_text or regex_test.

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 whenever an agent needs summary metrics about a text string, but it provides no explicit when-to-use or when-not-to-use guidance. No sibling tool overlaps significantly with text statistics, so the lack of alternatives is not a major problem, but the guidance is still only implicit.

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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