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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

text_word_frequency

Read-onlyIdempotent

Count how often each word appears in text and get a ranked frequency table with percentages. Options to ignore case, filter common words, set minimum length, sort by frequency or alphabetically, and limit results.

Instructions

Word Frequency Counter. Count how often each word appears in a block of text and return a ranked frequency table with per-word percentage and rank. Options let you fold case, drop common stop words, set a minimum word length, sort by frequency or alphabetically, and cap the number of rows returned. Use text_word_frequency when you only need a plain single-word frequency list with aggregate counts; use text_statistics for a full linguistic profile (readability scores, sentence and paragraph metrics) and word_counter for raw word, character, sentence, and paragraph totals without per-word breakdown. Runs locally on the text you provide: read-only, non-destructive, contacts no external service, and is rate-limited (60 requests/minute for anonymous callers). Returns the ranked results array plus summary statistics (total words processed, unique words, lexical diversity) and the effective options.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to analyze. Words are extracted as runs of letters and digits; punctuation is treated as a separator. Must not be blank.
caseSensitiveNoWhen true, treat differing letter case as distinct words; when false, lowercase every word before counting.
ignoreCommonWordsNoWhen true, exclude a built-in list of about 80 common English stop words (the, and, of, to, and similar) from the results.
minWordLengthNoMinimum character length a word must have to be counted; values above 1 filter out shorter words.
sortOrderNoResult ordering: frequency sorts most-frequent first; alphabetical sorts words A to Z.frequency
maxResultsNoMaximum number of word rows to return after sorting.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoWhether the analysis succeeded.
resultsNoRanked word rows, ordered per sortOrder and capped at maxResults.
statisticsNoAggregate metrics for the processed text.
optionsNoThe effective request options after defaults were applied.
Behavior5/5

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

Annotations already indicate read-only, non-destructive, and idempotent. The description adds valuable behavioral context: 'Runs locally on the text you provide... contacts no external service, and is rate-limited (60 requests/minute for anonymous callers).' This goes beyond what annotations provide.

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 well-structured: purpose, options list, usage alternatives, behavioral notes. It is appropriately sized and front-loaded with the core purpose. Minor redundancy with annotations, but not excessive.

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?

Given 6 parameters, 100% schema coverage, and presence of output schema, the description sufficiently covers the tool's function. It mentions return value (ranked results array plus summary stats) and effective options. Could include a brief example, but not necessary for completeness.

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?

Schema coverage is 100%, so the baseline is 3. The description paraphrases some options (fold case, drop stop words, etc.) but adds little new meaning beyond what the schema already describes. No additional parameter constraints or examples are provided.

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 clearly states the verb 'Count how often each word appears' and the resource 'block of text', and specifies the output 'ranked frequency table with per-word percentage and rank'. It also distinguishes from sibling tools by naming text_statistics and word_counter as alternatives.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides when to use this tool versus alternatives: 'Use text_word_frequency when you only need a plain single-word frequency list...; use text_statistics for a full linguistic profile... and word_counter for raw totals...' This gives clear guidance on context of use.

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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