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gunning_fog_score

Evaluate text readability by computing the Gunning Fog score, which estimates the years of education required for comprehension.

Instructions

Calculate Gunning Fog index for text readability.

Parameters:
    text — Text to calculate Gunning Fog score for.

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": "gunning_fog_score"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.4.1Jun 22, 2026

TDQS

C2.7/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states the tool calculates the Gunning Fog index, omitting any details about input constraints, format requirements, or result interpretation. This is insufficient for a tool that calculates a specific readability metric.

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?

The description is brief with two sentences, no wasted words, and front-loaded information. However, it may be too concise given the lack of behavioral context.

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?

Despite having a simple single-parameter input and an output schema, the description lacks essential context about the Gunning Fog index's meaning, interpretation, or usage scenarios. With many readability siblings, more completeness is needed to help the agent select the correct tool.

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?

The description reiterates the parameter name 'text' but adds little beyond the schema's title. It does not explain what constitutes valid text (e.g., length, language, formatting) or provide examples, so it adds minimal value.

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 that the tool calculates the Gunning Fog index for text readability, specifying a verb and resource. However, it does not distinguish itself from sibling readability tools like 'fry_readability' or 'smog_index', which limits its clarity in a crowded context.

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, nor does it mention any exclusions or prerequisites. Given the many sibling readability tools, this is a significant gap.

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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