atlas_pattern_learner

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

With no annotations, the description carries the full burden of behavioral disclosure. It mentions 'learns and adapts,' implying stateful behavior, but is vague about side effects, persistence, permissions, or return outcomes. It does not explain how learning occurs, whether it modifies stored knowledge, or what the agent should expect after calling the 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 concise, only two sentences, and front-loaded with the key purpose. Every sentence contributes to explaining what the tool does, with no redundant or filler content.

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 tool with five parameters and four distinct actions (learn, apply, suggest, report), the description is too high-level. It does not clarify what the different actions do, what output to expect, or how the tool integrates with the codebase, especially given the absence of an output schema. Significant gaps remain for an agent to invoke it 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?

Schema coverage is 100%, so the baseline is 3. The description adds some context by mentioning coding patterns and style preferences, which loosely align with the 'aspects' enum, but it does not elaborate on parameters like action, code, or projectPath beyond the schema descriptions. It adds marginal value.

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 'learns and adapts to coding patterns, style preferences, and architectural choices' and 'provides style-consistent suggestions,' making its primary function evident. It distinguishes itself from sibling tools by focusing on pattern learning and style adaptation rather than actions like refactoring or code review, though it does not explicitly name alternatives.

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 when the user needs style-consistent suggestions based on codebase patterns, but it does not provide explicit when-to-use conditions or contrast with sibling tools. There is no mention of when not to use it or prerequisites, leaving the guidance to inference.

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