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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.