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

Annotations are absent, so the description must cover behavioral traits. It does state that the AI analyzes breaking changes and suggests affected files/fix methods, and notes that without a changelog it uses general knowledge. However, it doesn't disclose potential side effects like reading files from project_root or whether modifications are suggested vs. executed.

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. Every phrase contributes to 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?

Given the output schema exists and parameters are documented, the description covers the main function but lacks usage context and comparison with sibling tools. It is minimal but not insufficient for an analysis tool; however, it could benefit from mentioning when to use it.

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?

All 6 parameters have descriptions in the schema (100% coverage), so the description adds no additional parameter semantics. The description doesn't mention how the parameters influence behavior beyond what schema already states.

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 that the tool analyzes breaking changes during package version upgrades and proposes affected files and modification methods. The specific verb 'analyze' and resource 'package upgrade' distinguish it from sibling tools like scan_dependencies and list_packages.

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 offers no guidance on when to use this tool instead of siblings, no mention of alternatives or typical scenarios. It only states what it does, leaving the agent to determine applicability.

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?

There are no annotations, so the description carries the full burden. It mentions only parsing and returning, without disclosing potential side effects, error handling, or limitations. The behavior is not contradictory but is under-specified.

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 core action. No wasted words.

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 with one parameter and an output schema exists, but the description lacks usage context or alternatives. It is minimally complete but would benefit from explaining when to use it versus sibling tools.

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 the single parameter at 100% with an example (requirements.txt, package.json). The description adds little beyond the schema, so baseline 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 it parses and returns the package list from a dependency file, using a specific verb and resource. It implies a narrower scope than sibling tools like scan_dependencies or analyze_upgrade, though it doesn't explicitly contrast with them.

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 over alternatives. The description only states what it does, leaving the agent to infer appropriate usage without any explicit context or exclusions.

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 does mention that AI analysis is part of the tool, which is a behavioral trait, but it does not disclose whether the tool is read-only, requires specific permissions, or sends data to external services. This leaves some ambiguity but avoids contradiction.

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 main action and includes no filler. It efficiently communicates the tool's function without unnecessary detail.

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 that an output schema exists and the input schema fully describes parameters, the description is reasonably complete for a straightforward scanner. It lacks explicit usage guidance and some behavioral nuance, but the core purpose is well-covered.

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 covers all three parameters with descriptions (100% coverage), so a baseline score of 3 applies. The main description does not add extra meaning beyond the schema, as it only refers to dependency files and AI analysis without detailing parameter syntax or constraints.

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 scans dependency files ('의존성 파일을 스캔') to detect vulnerabilities ('취약점을 탐지') and analyzes actual impact with AI ('AI로 실제 영향을 분석합니다'). This distinguishes it from sibling tools by emphasizing vulnerability scanning and AI 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 implies the tool is used for scanning dependencies for vulnerabilities, but it does not provide explicit guidance on when to use it versus alternatives like list_packages or analyze_upgrade. No exclusions or alternative recommendations are given, but the purpose is clear enough to imply usage.

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