APIVerve

Vowel Counter

VowelCounter

Read-only

Vowel Counter: Vowel Counter analyzes text to count vowels and consonants, providing detailed breakdowns and percentages for linguistic analysis. (Cost: 1 credit)

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": "VowelCounter"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	Text to analyze (max 50,000 characters)

Output Schema

Name	Required	Description
`data`	No
`error`	No	Error message, null on success
`status`	No	"ok" on success

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 7, 2026

TDQS

B3.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so safety is covered. The description usefully discloses the cost ('1 credit'), which annotations do not provide, and mentions percentages as part of the result, but says nothing about input limits or processing behavior beyond that.

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 single sentence is short but wastes its opening on a literal repeat of the tool name rather than front-loading the differentiator. The cost note is well placed at the end, but overall the text is marginally structured.

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?

For a one-parameter read-only tool with an output schema and full annotation coverage, the essentials are present. What is missing is routing guidance against the many similar text-analysis siblings, which matters given how crowded that namespace is.

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?

There is a single parameter and schema coverage is 100%, with the 50,000-character limit documented directly in the schema. The description adds no syntax, encoding, or edge-case meaning beyond 'analyzes text', so the baseline of 3 applies.

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 names a specific verb and resource (counts vowels and consonants) and adds that it produces breakdowns and percentages, so the operation is clear. However, it opens by restating the name verbatim ('Vowel Counter: Vowel Counter') and never distinguishes itself from text-analysis siblings like SyllableCounter or WordLexiconAnalyzer.

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 or when-not-to-use guidance, and no alternatives are named despite many adjacent character/word analysis tools in the sibling list. The agent must infer that this tool is chosen only when vowel/consonant statistics specifically are needed.

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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