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 'retry and model selection', it fails to describe critical behavioral aspects: what 'edit files' entails (e.g., file modifications, potential overwrites), how retries work, error handling, or output format details. For a tool with 14 parameters and no annotation coverage, this is a significant gap in transparency.

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 a single, efficient sentence that front-loads the core purpose ('Edit files via Claude Code CLI') and adds key features ('with retry and model selection'). There's no wasted verbiage, and it's appropriately sized for a tool with many parameters. However, it could be slightly more structured by explicitly separating purpose from features.

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 (14 parameters, no annotations, no output schema), the description is inadequate. It doesn't explain what the tool returns, how edits are applied, error conditions, or interaction with sibling tools. While the schema covers parameters, the description fails to provide the holistic context needed for safe and effective use, especially for a file-editing operation.

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%, meaning all parameters are documented in the input schema. The description adds minimal value beyond the schema—it mentions 'retry' (hinting at maxRetries) and 'model selection' (hinting at the model parameter), but doesn't provide additional context or usage examples. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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 action ('Edit files') and the mechanism ('via Claude Code CLI'), which is specific and actionable. It also mentions 'retry and model selection' as key features. However, it doesn't explicitly differentiate this tool from its siblings (claude_edit_json, claude_generate, claude_generate_json, claude_refactor), which would be needed for a score of 5.

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 its siblings or alternatives. It mentions 'retry and model selection' as features, but doesn't specify scenarios where this tool is preferred over claude_edit_json or claude_generate, nor does it mention any prerequisites or exclusions. This leaves the agent with insufficient context for optimal tool selection.

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 for behavioral disclosure. It mentions 'JSON response validation and retry' which hints at some behavior, but doesn't explain what happens when validation fails, what the retry mechanism entails, whether this is a read or write operation, what permissions might be needed, or what the output looks like. For a tool with 6 parameters and no annotation coverage, this is insufficient 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 extremely concise at just 6 words, with no wasted words or redundant information. It's front-loaded with the core concept. However, this conciseness comes at the cost of clarity - it's arguably too brief for a tool with 6 parameters and complex behavior involving validation and retry logic.

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 (6 parameters, JSON validation logic, retry mechanism) and the absence of both annotations and output schema, the description is incomplete. It doesn't explain what the tool actually produces, what validation errors look like, how retries work, or what the editing function entails. For a tool that presumably interacts with Claude AI models and performs JSON validation, this leaves critical gaps in understanding.

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 all 6 parameters thoroughly. The description adds no additional parameter information beyond what's in the schema - it doesn't explain how parameters interact (e.g., systemPrompt vs appendSystemPrompt), provide examples, or clarify edge cases. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

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 'Edit with JSON response validation and retry' is vague and tautological - it essentially restates the tool name 'claude_edit_json' without specifying what is being edited or what resource is involved. While it mentions JSON validation and retry mechanisms, it doesn't clearly state the core function (editing what? Claude responses? code? documents?). It fails to distinguish from siblings like 'claude_edit' or 'claude_refactor'.

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?

No guidance on when to use this tool versus alternatives is provided. The description doesn't explain when JSON validation is needed versus using 'claude_edit' without validation, or when to choose this over 'claude_generate_json'. There's no mention of prerequisites, error conditions, or typical use cases that would help an agent select this tool appropriately.

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. While it mentions 'retry and model selection' as features, it doesn't describe what 'Generate code or text' actually entails - whether this is a one-shot generation, conversational interaction, or something else. It doesn't mention authentication requirements, rate limits, cost implications, or what happens when the tool fails. For a complex tool with 14 parameters, this is a significant transparency gap.

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 a single, efficient sentence that gets straight to the point. It mentions the core action, the mechanism, and two key features. There's no wasted verbiage or unnecessary elaboration. However, it could be slightly more front-loaded by stating the primary purpose more prominently before mentioning implementation details.

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 complex tool with 14 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how errors are handled, or the nature of the generation process. The agent must rely entirely on the parameter names and schema descriptions to understand this tool's behavior, which is insufficient for a generation tool that likely produces variable outputs and has significant configuration options.

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?

With 100% schema description coverage, the input schema already documents all 14 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'retry' (which maps to maxRetries) and 'model selection' (which maps to model), but doesn't provide additional context about parameter interactions or best practices. The baseline of 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 action ('Generate code or text') and the mechanism ('via Claude Code CLI'), which is specific and actionable. However, it doesn't explicitly differentiate this tool from its siblings (claude_edit, claude_edit_json, claude_generate_json, claude_refactor), which all appear to be Claude-related generation/editing tools. The description mentions 'retry and model selection' which are implementation details rather than core purpose differentiation.

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 its siblings. There's no mention of when this generation tool should be chosen over claude_generate_json or claude_edit, nor any context about appropriate use cases. The agent must infer usage from tool names alone, which is insufficient for optimal tool selection.

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 full burden. It mentions 'validation and retry', which hints at error handling and retry logic, but doesn't disclose key behavioral traits such as what happens on validation failure (e.g., error messages, retry behavior details), rate limits, authentication needs, or output format specifics. This leaves significant gaps for a tool with validation features.

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 a single, efficient sentence: 'Generate JSON response with validation and retry'. It's front-loaded and wastes no words, though it could be slightly more informative. Every word earns its place, making it appropriately concise for the tool's complexity.

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 no annotations, no output schema, and 6 parameters, the description is incomplete. It doesn't explain the return values (e.g., JSON structure), error handling details, or how validation works with jsonSchema. For a tool focused on JSON generation with validation, more context is needed to understand its full behavior and output.

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 all 6 parameters thoroughly. The description adds no additional meaning beyond the schema, such as explaining interactions between parameters (e.g., how jsonSchema relates to validation) or usage examples. Baseline is 3 as 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 states the tool 'Generate JSON response with validation and retry', which indicates it generates JSON and includes validation/retry features. However, it doesn't specify what resource or content it generates JSON from (Claude's output), nor does it differentiate from siblings like claude_generate or claude_edit_json. The purpose is somewhat vague about the exact operation.

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?

No guidance is provided on when to use this tool versus alternatives like claude_generate (which might generate non-JSON) or claude_edit_json (which might edit existing JSON). The description implies JSON generation with validation, but there's no explicit context or exclusions mentioned for tool selection.

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. While it mentions 'retry and model selection,' it doesn't explain what 'refactor' entails operationally, whether this modifies files in place, creates backups, requires specific permissions, or has rate limits. For a code modification tool with zero annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that communicates the core functionality. It's appropriately sized for the tool's complexity, though it could be more front-loaded with the primary purpose before mentioning secondary features like retry and model selection.

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 complex code refactoring tool with 14 parameters and no annotations or output schema, the description is inadequate. It doesn't explain what 'refactor' means operationally, what the expected inputs/outputs are, how errors are handled, or how this differs from sibling editing tools. The 100% schema coverage helps, but the description should provide more contextual guidance for proper tool selection and 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?

Schema description coverage is 100%, so the schema already documents all 14 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'retry' (related to maxRetries) and 'model selection' (related to model parameter), but doesn't provide additional context about parameter interactions or usage patterns. Baseline 3 is appropriate when 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 states the tool 'refactor[s] code via Claude Code CLI' which provides a clear verb ('refactor') and resource ('code'), but it doesn't differentiate from sibling tools like claude_edit or claude_generate. The mention of 'retry and model selection' adds some specificity but doesn't clearly distinguish this as a refactoring-specific tool versus general code editing.

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?

No guidance is provided about when to use this tool versus alternatives like claude_edit or claude_generate. The description mentions 'retry and model selection' but doesn't explain when these features make this tool preferable. There's no mention of prerequisites, typical use cases, or comparison to sibling tools.

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