global_learn

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

With no annotations, description carries full burden. Discloses idempotency, no duplicates, no rate limits, storage prefix, recall via global_recall, and return of confirmation with key. All behavioral traits are transparent.

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?

Dense but efficient. Front-loaded with core purpose, then idempotency, returns, storage, usage, example, and sibling references. Slightly long but every sentence contributes.

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?

Despite 5 parameters and no output schema, description covers purpose, behavior, usage guidance, storage details, and return format. Example and sibling differentiation complete the picture.

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%, baseline 3. Description adds value via example showing topic format and usage, plus implied defaults. Does not detail each parameter but provides sufficient context beyond 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?

Clearly states 'Store a lesson that applies across ALL your projects', with specific verb 'store' and resource 'lesson'. Distinguishes from siblings by naming learn_from_attempts and team_learn.

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 use for 'tool preferences, personal workflows, platform quirks, and universal gotchas'. Directly tells when to use alternative tools: 'Use learn_from_attempts for project-specific session lessons; use team_learn to share lessons with your team.'

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