codelogic-method-impact

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 describes the tool's function as an impact analysis for code modifications, which implies it's a read-only analysis tool (not a mutation tool). However, it doesn't specify behavioral traits like whether it requires specific permissions, how it performs the analysis (e.g., static vs. dynamic), what the output format is, or any rate limits. The description adds some context but lacks detailed behavioral information.

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 well-structured and appropriately sized. It starts with a clear purpose statement, followed by a bullet-point workflow and a concluding note. Every sentence adds value: the first defines the tool, the workflow provides actionable guidance, and the last emphasizes importance in AI contexts. There's no redundant or wasted text, making it efficient and front-loaded.

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 (impact analysis for code changes), no annotations, and no output schema, the description is moderately complete. It covers purpose and usage well but lacks details on behavioral aspects (e.g., how analysis is performed, output format) and doesn't leverage structured fields. For a tool with no annotations or output schema, it should provide more context about what the analysis entails and what results to expect.

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% description coverage, with clear descriptions for both parameters ('class' and 'method'). The description doesn't add any semantic details beyond what the schema provides—it doesn't explain parameter formats, constraints, or examples. Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't need to given the schema's completeness.

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: 'Analyze impacts of modifying a specific method within a given class or type.' It specifies the verb ('analyze impacts') and resource ('specific method within a given class or type'), making the purpose unambiguous. However, it doesn't explicitly differentiate from its sibling tool 'codelogic-database-impact' beyond the domain difference implied by the names.

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 explicit usage guidelines in a recommended workflow format: 'Use this tool before implementing code changes,' 'Run the tool against methods or functions that are being modified,' and 'Particularly crucial when AI-suggested modifications are being considered.' This clearly indicates when to use the tool and provides context for its application, though it doesn't explicitly mention when not to use it or compare to the sibling tool.

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