MCP Utility Tools

text_stats

Count characters, words, sentences, and paragraphs in any text, and estimate the reading time to quickly assess its length and complexity.

Instructions

Get statistics about a piece of text: character, word, sentence, and paragraph counts, plus an estimated reading time.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "text_stats"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The text to analyze.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 11, 2026

TDQS

A4.1/5.0

Behavior3/5

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

No annotations are provided, so the description carries full burden. It clearly states the tool computes read-only statistics and lists the output categories. However, it does not disclose assumptions (e.g., reading time formula, Unicode handling) or explicitly affirm no side effects, leaving some behavioral gaps.

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, front-loaded sentence with no extraneous detail. It lists the key outputs without repetition and efficiently communicates the tool's behavior.

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?

Given the simple one-parameter tool and the presence of an output schema, the description adequately covers the tool's purpose for selection and invocation. It does not need to document return values since the output schema exists.

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 schema already provides 100% coverage for the sole parameter ('text' with description 'The text to analyze.'). The description adds no extra meaning beyond the schema, so it meets the baseline of 3.

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 ('Get') and identifies the resource ('statistics about a piece of text') while enumerating the exact outputs (character, word, sentence, paragraph counts, reading time). This clearly distinguishes it from sibling tools that perform conversions or hashing.

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 provides clear context that this tool is for analyzing text statistics and is unambiguously distinct from sibling utilities. It does not explicitly name alternatives or state when not to use it, but the intended use case is self-evident from the description.

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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