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readability

Score text for readability: Flesch reading ease, Flesch-Kincaid grade, average sentence length and syllable count, with a plain-English verdict. Pure CPU, insta

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": "readability"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText to score

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added
  2. Removed
  3. Changed2 schema fields changed
    • addedInput schema / properties / text
      Added value: +{
+  "description": "Text to score",
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[
+  "text"
+]
  4. Added

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose that this is a pure-CPU, instant, local computation with no external dependency — useful for an agent judging latency and side effects. However, it says nothing about limits (input size, language assumptions) or whether it is read-only, and the sentence trails off mid-word, so the disclosure is incomplete.

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

Conciseness3/5

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

The metrics are front-loaded and the sentence is dense but readable, with no filler. The final clause is truncated mid-word ('insta'), which both looks unpolished and cuts off a claim the agent may need.

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

Completeness3/5

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

Because there is no output schema and no annotations, the description must define the return values, and it largely does by naming the individual scores and the verdict. It is still incomplete on language support, input length limits, and — most importantly — how it differs from 'analyze_readability', and the truncation leaves the last point unfinished.

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?

There is a single parameter ('text') with 100% schema description coverage, so the schema fully documents it and the baseline is a 4. The description adds only that the text is scored for the listed metrics, which is consistent but not extra semantics.

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?

Names a specific verb ('Score') and resource ('text for readability') and enumerates the concrete metrics returned: Flesch reading ease, Flesch-Kincaid grade, average sentence length, syllable count, plus a verdict. That is far more specific than a tautology. It loses a point because the sibling tool 'analyze_readability' appears to do the same thing and the description never distinguishes them.

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?

There is no when-to-use guidance and no mention of alternatives, which is a real problem given a near-duplicate sibling ('analyze_readability') exists. 'Pure CPU' hints that it is local and fast rather than calling an external service, but that is incidental rather than routing advice.

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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