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Aaryan-Kapoor

MCP Character Tools

Count Letter

count_letter
Read-onlyIdempotent

Count occurrences of a specific letter in text, returning positions, visual breakdown, and density percentage.

Instructions

Count occurrences of a specific letter in text.

Returns the count, positions, a visual breakdown showing where each letter appears, and a density percentage.

Args:

  • text (string): The text to analyze

  • letter (string): The single letter to count

  • case_sensitive (boolean): Whether to match case exactly (default: false)

Returns: count, positions array, visual breakdown, and density summary.

Example: count_letter("strawberry", "r") → count: 3, positions: [2, 5, 8]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe text to analyze
letterYesThe letter to count
case_sensitiveNoMatch case exactly
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive. Description adds behavioral details: returns count, positions, visual breakdown, density. No contradiction.

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?

Well-structured with sections: summary, return details, Args, Returns, Example. No redundant sentences, efficient.

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?

Complete for a 3-param tool with no output schema. Explains what it does and returns. Could mention edge cases like empty text, but schema has minLength constraints.

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?

Schema coverage is 100% with good descriptions. Description adds value with example and explanation of return structure, but schema already covers parameter meanings.

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?

Description clearly states it counts occurrences of a specific letter in text, with a specific verb and resource. It distinguishes from siblings like 'count_letters' (plural) and 'count_substring' (different entity).

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?

Description implies usage for single letter counting with example, but does not explicitly state when not to use or compare to siblings. It provides clear context for its intended use case.

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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