Text-Transformer

countWords

countWords

Count words in text to analyze content length, track writing progress, or meet character limits. This tool processes any input string and returns the total word count.

Instructions

Count the number of words in text

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": "countWords"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 5, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool does but doesn't describe how it behaves: no information on what constitutes a 'word' (e.g., handling of punctuation, whitespace), error handling, performance characteristics, or output format. This leaves significant gaps for an agent to understand 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 extremely concise with a single sentence that directly states the tool's purpose. It is front-loaded with no wasted words, making it efficient for quick understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits (e.g., word definition), usage context compared to siblings, and output specifics, which are necessary for an agent to use it correctly without guesswork.

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 description adds minimal meaning beyond the input schema. It mentions 'text' as the input, which aligns with the schema's single parameter, but doesn't provide additional context like expected format, length limits, or examples. With 0% schema description coverage, the description doesn't fully compensate, but it at least confirms the parameter's purpose.

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?

The description clearly states the tool's function as 'Count the number of words in text', which is a specific verb ('Count') applied to a resource ('words in text'). It distinguishes from siblings like 'countCharacters' by specifying words rather than characters, but doesn't explicitly mention the distinction.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. While the name implies word counting, it doesn't specify scenarios where this is preferred over character counting or other text analysis tools, nor does it mention any prerequisites or limitations.

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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