get_pitfalls

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

No annotations are provided, so the description must convey behavioral traits. It describes the tool as returning pitfalls, implying a read-only operation. However, it doesn't disclose potential side effects, rate limits, or authentication needs. The description is adequate but not rich in behavioral details.

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 concise with a clear purpose statement, usage guidance, and parameter details. It is front-loaded with the main intent and contains no superfluous 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?

Given the tool has only two optional parameters and no output schema, the description provides sufficient context: purpose, usage, and parameter options. It doesn't describe the return format, but for a list of pitfalls, this is likely acceptable. Overall, it is complete enough for an agent to use correctly.

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 0% description coverage, so the description compensates by listing allowed values for category and ai_source in the Args section. It explains that category can be one of several enumerated values and ai_source can be 'kiro', 'claude-code', or 'both', adding significant meaning 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 clearly states the tool returns known LSL pitfalls for AI coding assistants. It specifies the verb 'Return' and the resource 'known LSL pitfalls'. It also differentiates from sibling tools like check_code and get_constants by focusing specifically on pitfalls.

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 explicitly recommends calling with no arguments for a full briefing before starting an LSL task. It also explains how to filter by category or ai_source. While it doesn't explicitly state when not to use it, the usage advice is clear and contextual.

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