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Japanese Text Analyzer

by Mistizz
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Japanese Text Analyzer MCP Server

This is an MCP server that can perform morphological analysis of Japanese text. It measures and evaluates the characteristics of sentences from a linguistic perspective, and is useful for providing feedback on sentence generation.

function

  • Count the number of characters in Japanese text (the actual number of characters excluding spaces and line breaks)

  • Count the number of words in Japanese text

  • Detailed analysis of linguistic features of Japanese texts (average sentence length, proportion of parts of speech, vocabulary diversity, etc.)

  • Supports both file path and direct text input

  • Flexible file path resolution (absolute path, relative path, or file name only can be searched)

Related MCP server: Text Analyzer

Tools

Currently the following tools are implemented:

count_chars

Measures the number of characters in a file. Specify the absolute path (either Windows format C:\Users... or WSL/Linux format /c/Users/... is OK). The actual number of characters will be counted, excluding spaces and line breaks.

input:

  • filePath (string): The path to the file to count characters in (preferably Windows or WSL/Linux absolute path).

output:

  • Number of characters in the file (actual number of characters excluding spaces and line breaks)

count_words

Counts the number of words in a file. Specify an absolute path (either Windows style C:\Users... or WSL/Linux style /c/Users/... is acceptable). For English it counts space-separated words, for Japanese it uses morphological analysis.

input:

  • filePath (string): The path to the file to count words in (preferably Windows or WSL/Linux absolute path).

  • language (string, optional, default: "en"): Language of the file (en: English, ja: Japanese)

output:

  • Word count of the file

  • In Japanese mode, detailed morphological analysis results are also displayed.

count_clipboard_chars

Measures the number of characters in a text. Counts the actual number of characters excluding spaces and line breaks.

input:

  • text (string): The text to count characters in.

output:

  • Number of characters in the text (actual number of characters excluding spaces and line breaks)

count_clipboard_words

Counts the number of words in a text. In English it counts words separated by spaces, in Japanese it uses morphological analysis.

input:

  • text (string): The text to count words in.

  • language (string, optional, default: "en"): Language of the text (en: English, ja: Japanese).

output:

  • Number of words in the text

  • In Japanese mode, detailed morphological analysis results are also displayed.

analyze_text

Perform detailed morphological and linguistic feature analysis of text, including sentence complexity, parts of speech ratio, vocabulary diversity, etc.

input:

  • text (string): The text to analyze.

output:

  • Basic information about the text (total number of characters, number of sentences, total number of morphemes)

  • Detailed analysis results (average sentence length, proportion of parts of speech, proportion of character types, vocabulary diversity, etc.)

analyze_file

We perform in-depth morphological and linguistic feature analysis of your files, including sentence complexity, parts of speech ratio, lexical diversity, and more.

input:

  • filePath (string): The path to the file to analyze (preferably an absolute path in Windows or WSL/Linux format).

output:

  • Basic information about the file (total number of characters, number of sentences, total number of morphemes)

  • Detailed analysis results (average sentence length, proportion of parts of speech, proportion of character types, vocabulary diversity, etc.)

How to use

Running with npx

This package can be run with npx directly from the GitHub repository:

npx -y github:Mistizz/mcp-JapaneseTextAnalyzer

Use with Claude for Desktop

Add the following to your Claude for Desktop config file:

Windows: %AppData%\Claude\claude_desktop_config.json

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

Use with Cursor

For Cursor, add the same settings to the mcp.json file in the .cursor folder.

Windows: %USERPROFILE%\.cursor\mcp.json

macOS/Linux: ~/.cursor/mcp.json

Common configuration (works on most environments):

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "npx",
      "args": [
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

If the above doesn't work on Windows, try the following:

{
  "mcpServers": {
    "JapaneseTextAnalyzer": {
      "command": "cmd",
      "args": [
        "/c",
        "npx",
        "-y",
        "github:Mistizz/mcp-JapaneseTextAnalyzer"
      ]
    }
  }
}

Usage Example

Count characters directly in text

このテキストの文字数を数えてください。

Count the number of words in a file in Japanese mode

C:\path\to\your\file.txt の単語数を日本語モードで数えてください。

Count words in a WSL/Linux style path

/c/Users/username/Documents/file.txt の単語数を日本語モードで数えてください。

Count words in filename only

README.md の単語数を英語モードで数えてください。

Paste text and count Japanese words

次のテキストの日本語の単語数を数えてください：

吾輩は猫である。名前はまだ無い。どこで生れたかとんと見当がつかぬ。何でも薄暗いじめじめした所でニャーニャー泣いていた事だけは記憶している。

Analyze detailed linguistic features of text

次のテキストを詳細に分析してください：

私は昨日、新しい本を買いました。とても面白そうな小説で、友人からの評判も良かったです。今週末にゆっくり読む予定です。

Analyze the detailed linguistic features of the file

C:\path\to\your\file.txt を詳細に分析してください。

File path resolution function

This tool has the flexibility to find files when a file path is specified:

  1. If an absolute path is specified, it is used as is.

    • Absolute path in Windows format (e.g. C:\Users\username\Documents\file.txt )

    • Both WSL/Linux style absolute paths (e.g. /c/Users/username/Documents/file.txt ) are automatically detected and converted.

  2. Resolve relative paths based on the current directory (working directory)

  3. Search based on home directory ( %USERPROFILE% or $HOME )

  4. Search based on the Desktop directory

  5. Search by document directory

This means that even if you simply specify a file name such as "README.md", it will automatically search in several common directories and use the file if it is found. Also, paths obtained from WSL environments, Git Bash, etc. (in /c/Users/... format) can be used as is in Windows environments.

Under the hood

This tool uses a morphological analysis library called "kuromoji.js" to count the number of Japanese words. Morphological analysis is a basic process in natural language processing, which divides a sentence into the smallest units of meaning (morphemes).

The morphological analysis process can take some time to initialize. In particular, it may take some time the first time you run it, since it needs to load dictionary data. By initializing the morphological analyzer when the server starts, we minimize delays when the tool is running.

Analysis of linguistic features

The "analyze_text" and "analyze_file" tools calculate various linguistic features of the text based on the results of the morphological analysis. These include the following metrics:

  • Average Sentence Length : The average number of characters per sentence. The higher this value, the harder the text may be to read.

  • Morphemes per sentence : The average number of morphemes per sentence. This indicates sentence density and syntactic complexity.

  • Parts of speech : Shows the proportion of parts of speech (nouns, verbs, adjectives, etc.) used in the text.

  • Particle Proportions : Shows how frequently certain particles are used and analyzes sentence structure and flow.

  • Proportion of character types : Shows the composition ratio of hiragana, katakana, kanji, and alphanumeric characters.

  • Lexical diversity : A measure of vocabulary richness by showing the ratio of different words to the total number of words (types/tokens ratio).

  • Proportion of Katakana words : Indicates the frequency of use of Katakana words, reflecting the prevalence of foreign words and technical terms, and the casualness of the writing style.

  • Honorific Language Frequency : Indicates how often honorific expressions are used and measures how polite or formal the text is.

  • Average number of punctuation marks : The average number of punctuation marks per sentence provides an indication of sentence division and readability.

By combining these indicators, we can analyze the characteristics of a text from multiple angles and evaluate its writing style, readability, expertise, etc.

license

This MCP server is provided under the MIT license, which means you are free to use, modify and distribute the software according to the terms of the MIT license. For more information, see the LICENSE file in the project repository.

Available Tools

6 tools
analyze_fileC

ファイルの詳細な形態素解析と言語的特徴の分析を行います。文の複雑さ、品詞の割合、語彙の多様性などを解析します。

ParametersJSON Schema
NameRequiredDescriptionDefault
filePathYes分析するファイルのパス（Windows形式かWSL/Linux形式の絶対パスを推奨）

TDQS

C2.9/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions what the tool analyzes but doesn't describe how it behaves: whether it's read-only or modifies files, what permissions are needed, error handling, performance characteristics, or output format. For a file analysis tool with zero annotation coverage, this is a significant gap in behavioral context.

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 appropriately concise with two sentences that efficiently convey the tool's purpose and analysis scope. It's front-loaded with the core function and avoids unnecessary elaboration, though it could potentially benefit from slightly more structure to separate different aspects of analysis.

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

Completeness2/5

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

Given the complexity of file analysis (involving file I/O, linguistic processing) with no annotations and no output schema, the description is incomplete. It doesn't explain what the analysis returns, error conditions, file format requirements, or behavioral constraints. For a tool that presumably performs non-trivial linguistic analysis on files, more context is needed for effective agent use.

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?

The schema description coverage is 100%, with the single parameter 'filePath' well-documented in the schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline of 3 where the schema does the heavy lifting without compensating with additional semantic context.

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 clearly states the tool performs 'detailed morphological analysis and linguistic feature analysis' of files, specifying it analyzes sentence complexity, part-of-speech ratios, and lexical diversity. This provides a specific verb ('analyze') and resource ('files'), though it doesn't explicitly differentiate from sibling tools like 'analyze_text' which might analyze text directly rather than files.

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?

The description provides no guidance on when to use this tool versus alternatives like 'analyze_text' or the counting tools. It doesn't mention prerequisites, file format requirements, or any context for choosing this tool over siblings, leaving the agent to infer usage based on the name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

analyze_textC

テキストの詳細な形態素解析と言語的特徴の分析を行います。文の複雑さ、品詞の割合、語彙の多様性などを解析します。

ParametersJSON Schema
NameRequiredDescriptionDefault
textYes分析するテキスト

TDQS

C2.9/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions what analyses are performed, it doesn't describe output format, performance characteristics, error conditions, or any limitations (e.g., text length constraints, language support). For a tool with no annotation coverage, this leaves significant behavioral aspects undocumented.

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 appropriately concise with two sentences that efficiently convey the tool's purpose and specific analyses. Every sentence contributes meaningful information without redundancy, though it could be slightly more structured with clearer separation of core function versus analysis types.

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

Completeness2/5

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

Given the tool's complexity (linguistic analysis with multiple metrics) and lack of both annotations and output schema, the description is incomplete. It doesn't explain what the analysis results look like, how they're structured, or what users can expect from the output. For a tool performing detailed analysis without output documentation, this creates significant ambiguity for proper usage.

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?

The schema description coverage is 100%, with the single parameter 'text' clearly documented in the schema as 'text to analyze'. The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3 for high schema coverage.

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 clearly states the tool performs 'detailed morphological analysis and linguistic feature analysis' of text, specifying specific analyses like sentence complexity, part-of-speech ratios, and lexical diversity. It uses specific verbs ('analyzes') and resources ('text'), but doesn't explicitly differentiate from sibling tools like 'analyze_file' or 'count_words'.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'analyze_file' (for file-based analysis) or 'count_words' (for simpler counting), nor does it specify contexts where detailed linguistic analysis is preferred over basic counting operations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

count_charsA

ファイルの文字数を計測します。絶対パスを指定してください（Windows形式 C:\Users...、またはWSL/Linux形式 /c/Users/... のどちらも可）。スペースや改行を除いた実質的な文字数をカウントします。

ParametersJSON Schema
NameRequiredDescriptionDefault
filePathYes文字数をカウントするファイルのパス（Windows形式かWSL/Linux形式の絶対パスを推奨）

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it requires absolute paths, accepts two OS path formats, and excludes whitespace from counts. However, it doesn't mention error handling, file size limits, encoding considerations, or what happens with non-existent files - important gaps for a file operation tool.

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?

Three efficient sentences with zero waste. First states purpose, second specifies path requirements with examples, third clarifies counting behavior. Each sentence adds distinct, necessary information. The description is appropriately sized and front-loaded with the core functionality.

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 single-parameter tool with good schema coverage but no annotations or output schema, the description is adequate but has gaps. It covers the what and how of character counting but lacks information about return values, error conditions, or performance characteristics. Given the complexity (file operations can fail in many ways), more behavioral context would be helpful.

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 description coverage is 100% with one parameter, so baseline would be 3. The description adds meaningful context: it emphasizes the absolute path requirement, specifies acceptable formats (Windows or WSL/Linux), and explains this is for character counting. This provides practical guidance beyond the schema's basic parameter documentation.

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 specific action ('計測します' - measure/count) and resource ('ファイルの文字数' - file character count). It distinguishes from sibling tools like count_words (words vs characters), count_clipboard_chars (clipboard vs file), and analyze_file/analyze_text (analysis vs counting).

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 provides clear context about when to use this tool: for counting characters in files with absolute paths. It specifies acceptable path formats (Windows or WSL/Linux) and that it counts '実質的な文字数' (substantial characters) excluding spaces and line breaks. However, it doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

count_clipboard_charsB

テキストの文字数を計測します。スペースや改行を除いた実質的な文字数をカウントします。

ParametersJSON Schema
NameRequiredDescriptionDefault
textYes文字数をカウントするテキスト

TDQS

B3.3/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It adds useful behavioral context by specifying that it counts '実質的な文字数' (substantial characters) excluding spaces and line breaks, which clarifies the counting method. However, it doesn't disclose other traits like error handling, performance, or output format.

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 extremely concise and front-loaded, consisting of just two sentences that directly state the tool's purpose and counting method. Every sentence earns its place by providing essential information without redundancy or fluff.

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?

Given the tool's low complexity (one parameter, no output schema, no annotations), the description is minimally adequate. It explains what the tool does and the counting method, but lacks details on output format, error cases, or differentiation from siblings. For a simple tool, this is acceptable but leaves gaps.

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?

The schema description coverage is 100%, with the parameter 'text' clearly documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

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 clearly states the tool's purpose: 'テキストの文字数を計測します' (counts characters in text). It specifies the verb (計測/measure) and resource (テキスト/text). However, it doesn't explicitly distinguish itself from sibling tools like 'count_chars' or 'count_clipboard_words', which likely have similar functions.

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?

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'count_chars' or 'count_clipboard_words', nor does it specify contexts or exclusions for its use. The agent must infer usage from the tool name and description alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

count_clipboard_wordsA

テキストの単語数を計測します。英語ではスペースで区切られた単語をカウントし、日本語では形態素解析を使用します。

ParametersJSON Schema
NameRequiredDescriptionDefault
languageNoテキストの言語 (en: 英語, ja: 日本語)en
textYes単語数をカウントするテキスト

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the counting methodology for different languages, which is useful context. However, it doesn't mention performance characteristics, error handling, or output format. For a tool with no annotations, this is adequate but lacks depth.

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 extremely concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first states what the tool does, the second explains language-specific behavior. Zero waste.

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 simple counting tool with no annotations and no output schema, the description is minimally complete. It explains what the tool does and language handling, but doesn't describe the return value format. Given the low complexity, this is adequate but could benefit from output information.

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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 tool's purpose: 'テキストの単語数を計測します' (counts words in text). It specifies the verb (計測/measure) and resource (単語数/word count), and distinguishes from siblings like count_chars and count_clipboard_chars by focusing on words rather than characters.

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 provides clear context for when to use this tool: for counting words in text, with specific language handling (English uses space separation, Japanese uses morphological analysis). However, it doesn't explicitly mention when NOT to use it or name alternatives among siblings like count_words.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

count_wordsA

ファイルの単語数を計測します。絶対パスを指定してください（Windows形式 C:\Users...、またはWSL/Linux形式 /c/Users/... のどちらも可）。英語ではスペースで区切られた単語をカウントし、日本語では形態素解析を使用します。

ParametersJSON Schema
NameRequiredDescriptionDefault
filePathYes単語数をカウントするファイルのパス（Windows形式かWSL/Linux形式の絶対パスを推奨）
languageNoファイルの言語 (en: 英語, ja: 日本語)en

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: it accepts both Windows and WSL/Linux path formats, uses space-based counting for English and morphological analysis for Japanese. However, it doesn't mention error handling, file size limits, performance characteristics, or what the output looks like (just a number? JSON structure?).

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 perfectly concise and front-loaded. The first sentence states the core purpose, followed by essential implementation details. Every sentence earns its place: path format requirements, language-specific counting methods. No wasted words or redundancy.

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 2-parameter tool with no annotations and no output schema, the description is adequate but has gaps. It covers the core functionality and parameter usage well, but doesn't describe the return value format or error conditions. Given the complexity (language-specific counting algorithms) and lack of output schema, more information about what the tool returns would be helpful.

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 description coverage is 100%, so the schema already documents both parameters well. The description adds some value by explaining the language-specific counting methods (space-based for English, morphological analysis for Japanese) and emphasizing the absolute path requirement with format examples. However, it doesn't add significant semantic information beyond what's in the 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 clearly states the tool's purpose: 'ファイルの単語数を計測します' (counts words in a file). It specifies the verb ('計測します' - measures/counts) and resource ('ファイル' - file), and distinguishes from sibling tools like count_chars (character counting) and analyze_file/analyze_text (more general analysis).

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 provides clear context for when to use this tool: for counting words in files with specific path formats. It doesn't explicitly state when NOT to use it or name alternatives, but the sibling tool names (count_chars, analyze_file, etc.) suggest differentiation by function. The language parameter guidance also helps determine appropriate usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 6 tool updatesv1.0.0
    • First observedanalyze_file
    • First observedanalyze_text
    • First observedcount_chars
    • First observedcount_clipboard_chars
    • First observedcount_clipboard_words
    • First observedcount_words
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TDQS

B3.3/5.0
Disambiguation2/5

There is significant ambiguity between tools: analyze_file and analyze_text appear to do the same analysis but on different inputs (file vs. text), while count_chars and count_clipboard_chars both count characters with similar descriptions, and count_words and count_clipboard_words both count words with similar logic. This overlap can easily lead to misselection by an agent.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern throughout (e.g., analyze_file, count_chars), with all tools using snake_case. However, the use of 'clipboard' in some names deviates slightly from the file/text distinction in others, but overall the pattern is predictable and readable.

Tool Count4/5

With 6 tools, the count is reasonable for a text analysis server, as it covers multiple aspects like analysis and counting. It's slightly on the higher side due to duplication, but not excessive, and each tool serves a purpose within the domain.

Completeness3/5

The server covers core text analysis and counting functions, but there are notable gaps: it lacks tools for operations like summarization, sentiment analysis, or editing, which are common in text processing. The surface is functional but not comprehensive for advanced text analysis workflows.

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