Skip to main content
Glama

CutGPT Research & Fact-Check

Count text exactly

text_stats
Read-onlyIdempotent

Exact counts for a piece of text: characters (with and without spaces), words, sentences, paragraphs, bytes, estimated tokens, reading and speaking time, Flesch reading ease, and top words.

Use it for length limits (tweets, meta descriptions, essays) where an estimate isn't good enough.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered without the description. The description adds only the notion of exactness versus estimation; it says nothing about input-format expectations, size limits, or failure behavior. With annotations carrying the burden, a 3 is appropriate.

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

Conciseness4/5

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

Two sentences, zero preamble: the metric list comes first and the usage condition second, which is the right ordering. The metric enumeration is dense and partly redundant with the existing output schema, but it is front-loaded and readable rather than wasteful.

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?

An output schema exists, so the description needn't explain return values, and the annotations cover the safety profile for this trivial read-only tool. The only remaining gap is input-format/size guidance, which is minor for a one-parameter counting tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter "text" has 0% schema description coverage, but it is self-evident, and the description's metric list (words, sentences, paragraphs) implies the input is raw prose rather than markup. It does not, however, clarify plain-text vs HTML handling or mention the 500,000-character cap the schema enforces.

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 verb (count) and resource (a piece of text) and enumerates exactly what is measured — characters, words, sentences, paragraphs, bytes, tokens, reading time, Flesch ease, top words. No sibling tool (search_papers, read_url, wikipedia_lookup, etc.) does counting, so the agent can route here unambiguously.

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?

"Use it for length limits (tweets, meta descriptions, essays) where an estimate isn't good enough" gives a concrete when-to-use condition and a reason to prefer it over an estimator. It stops short of naming an alternative tool or an explicit when-not-to-use case, but none of the siblings overlap, so little is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources