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Aaryan-Kapoor

MCP Character Tools

Batch Count

batch_count
Read-onlyIdempotent

Count occurrences of a given letter across multiple words, with optional case sensitivity, and receive per-word results and totals sorted by count.

Instructions

Count a letter across multiple words at once.

Efficiently process a list of words.

Args:

  • words (string[]): Array of words to analyze

  • letter (string): The letter to count

  • case_sensitive (boolean): Match case exactly (default: false)

Returns: Results for each word, totals, sorted by count.

Example: batch_count(["strawberry", "raspberry", "blueberry"], "r") → individual and total counts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
wordsYesWords 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. The description adds sorting by count, individual and total results, and an example. No contradictions.

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 brief yet informative, front-loaded with the main action, and includes an example. Every sentence adds value.

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

Completeness5/5

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

Given the tool's low complexity and full schema coverage, the description covers purpose, parameter meanings, behavior (sorting, totals), and example. No gaps.

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%, so baseline 3. The description adds meaning by explaining the default for case_sensitive and the overall purpose of each parameter, going slightly beyond schema descriptions.

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?

The description explicitly states 'Count a letter across multiple words at once' with a clear verb and resource. It distinguishes from siblings like count_letter (single word) by emphasizing batch processing.

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?

The description implies when to use (multiple words) but does not explicitly state when not to use or reference alternatives. It is clear enough for context.

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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