code-explainer

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 states that all five analyses are run, but does not disclose what the combined output looks like, whether it returns a single object or multiple, or any performance implications. It lacks detail about the response format and potential partial-failure behavior.

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 sentence that front-loads the main purpose, then lists the five analyses. Every word earns its place; no redundancy or filler.

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?

The tool is an aggregation of five analyses, which is moderately complex, and there is no output schema. The description explains what the tool runs but does not describe the shape of the response or how the combined results are organized. It is complete enough for basic usage but lacks expected return-value details.

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 covers both parameters (code and language) with descriptions, so the baseline is 3. The description does not add any additional meaning to the parameters, but given 100% schema coverage, no compensation is needed.

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 runs all five analyses in one call and explicitly lists them, using a specific verb ('Run') and resource ('all five analyses'). It distinguishes itself from the sibling tools, which handle individual analyses.

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 clearly implies when to use this tool: when you want all five analyses at once. However, it does not explicitly contrast with the individual sibling tools or state when not to use it (e.g., 'for a single analysis, use the specific tool instead').

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. It states what metrics are calculated but does not describe the output format, side effects, error handling, or any limitations. This is minimal transparency for an analysis 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?

The description is a single, front-loaded sentence that begins with the verb+resource pair and then enumerates relevant metrics. Every word earns its place with no 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?

With no output schema and no annotations, the description should explain return format or behavior, but it does not. It also lacks usage exclusions. However, the parameter schema is complete and the tool's scope is simple, making it adequate for basic understanding but incomplete for fully autonomous invocation.

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 clear descriptions for both 'code' and 'language' parameters. The tool description adds no parameter-specific semantics beyond the schema, so the baseline of 3 applies.

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 uses the specific verb 'Analyze' with resource 'code complexity' and explicitly lists four distinct metrics (cyclomatic complexity, cognitive complexity, nesting depth, maintainability index). This clearly distinguishes it from siblings like detect_language or explain_code, making the purpose unambiguous.

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 implies usage for code complexity analysis, but provides no explicit when-to-use, exclusions, or alternative pointers. It does not differentiate from analyze_all or other siblings beyond the stated purpose, so usage guidance is merely implied.

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, the description carries full responsibility for behavioral disclosure. It states that it returns language and confidence but does not describe limitations, supported languages, or error behavior for ambiguous snippets.

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 two short sentences that are direct and free of fluff. Every word contributes to the tool's purpose and return behavior.

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 single-parameter tool, the description is adequate but lacks context on language coverage, edge cases, or the exact response structure. Without an output schema, the return format is only vaguely described.

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 input schema fully documents the single 'code' parameter, so the description adds minimal additional semantic value. The description's mention of 'code snippet' aligns with the schema, but doesn't add new meaning beyond what's already stated.

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 uses the specific verb 'Detect' with the resource 'programming language of a code snippet', clearly distinguishing it from sibling tools like explain_code or analyze_complexity. It also mentions the return value (language and confidence), making the purpose unambiguous.

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. The description does not mention any constraints, alternatives, or typical use cases beyond the obvious function.

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 behavioral disclosure burden. It states the output includes natural language explanation, structural analysis, and summary, which is useful, but it does not mention side effects, permissions, or error handling for invalid code. Given the likely read-only nature, this is a moderate 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, concise sentence that front-loads the action and output. Every word contributes meaning, with no redundancy or filler.

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 (2 parameters, no nested objects, no output schema), the description provides sufficient context for basic invocation. It covers the core purpose and output content, though it lacks explicit guidance on tool selection and leaves auto-detection of language to the schema. Overall, it is fairly complete for a simple tool.

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 coverage is 100%, with clear descriptions for both parameters (code and language). The description adds no additional parameter semantics beyond what the schema already provides, so the baseline 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 uses a specific verb ('Generate') and resource ('code snippet'), and specifies the output scope ('structural analysis, features, and summary'). This clearly distinguishes it from siblings like generate_docstring or suggest_refactor, which have different output types.

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 implies usage for explaining code snippets but provides no explicit when-to-use or when-not-to-use guidance. It does not compare with sibling tools such as analyze_all or generate_docstring, leaving the agent without clear selection criteria.

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 responsibility for disclosing behavior. It does not state whether the tool returns the generated docstring text as output, whether it modifies the input code, or how it handles multiple functions or invalid input. The auto-detection behavior mentioned in the schema is not reflected in the description.

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, 15-word sentence that is front-loaded and contains no extraneous information. It efficiently conveys the core purpose and supported formats.

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?

With no output schema, the description should explain what the tool returns (e.g., the generated docstring text) but does not. It also omits mention of the optional language auto-detection behavior, leaving the tool's operational expectations incomplete for an agent.

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 input schema has 100% coverage with descriptions for both 'code' and 'language'. The description adds no additional parameter semantics beyond what the schema already provides, so 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 uses a specific verb 'Generate' and clearly identifies the resource: doc comments for functions. It lists four specific output formats (JSDoc, Python docstring, Go godoc, Rust doc comments), distinguishing it from sibling tools like analyze_all, explain_code, or suggest_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?

The description implies the tool is for generating doc comments, but it does not explicitly state when to use it versus alternatives such as analyze_complexity or explain_code. No exclusionary guidance is provided; the usage context is inferred from the tool's name and purpose.

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, the description carries the burden. It indicates the tool returns suggestions rather than making changes, which is a useful behavioral trait, but it lacks details on processing, output format, or any caveats.

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, front-loaded sentence with no filler. It efficiently communicates the core purpose.

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?

The tool is simple (2 params, no output schema), but the description does not mention the output format or clarify that it is non-destructive, which could be important for an AI agent. It is adequate but has 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?

Schema description coverage is 100%, so both parameters are already documented. The description adds context about intent but no additional semantic details beyond the schema.

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 'Get prioritized refactoring suggestions for code quality improvements' clearly identifies the action (get suggestions), the resource (code refactoring), and a specific trait (prioritized). It is distinct from siblings like explain_code and analyze_complexity.

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 implies the tool is for code quality improvements but does not explicitly state when to use it over alternatives, nor does it mention exclusions. Sibling tools exist, but no comparison is made.

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