import_public_brain

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

Despite no annotations, the description fully discloses non-destructive behavior (existing lessons not overwritten) and return values (count and topic slugs). Also enumerates compatible frameworks.

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?

Description is concise with two sentences and a list, front-loaded with the main action. Slight redundancy in listing frameworks inline could be optimized, but overall efficient.

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 fully covers return format, non-destructive guarantee, required parameters, and available framework options. Completes all necessary context for agent 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 coverage is 100% with descriptions for all three parameters. The description adds little beyond the schema, such as the default limit of 20 and explicit framework list, but does not significantly enhance parameter understanding.

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?

Clearly states the tool imports community lessons for a framework, lists available frameworks, and specifies it's non-destructive. Differentiates from sibling tools publish_lesson and learn_from_attempts.

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?

Explicitly advises to use 'to bootstrap a new brain with battle-tested community knowledge before your first session_start' and contrasts with publish_lesson and learn_from_attempts, providing clear when-to-use and alternatives.

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