global_learn

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

With no annotations, the description fully covers idempotency ('if a lesson with the same topic already exists, it is updated in place'), the prefix used for storage, no rate limits, and return value. It lacks error handling details but is otherwise 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?

Three sentences plus an example, no wasted words. The main purpose is front-loaded, and the structure is logical (what it does, how it works, when to use, example, alternatives).

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 no output schema, the description explains the return value (confirmation with lesson key). It covers scope, idempotency, prefixes, rate limits, and sibling tool distinctions. Complete for a store operation.

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 baseline is 3. The description adds value by explaining the topic format ('category:keyword'), default severity ('minor'), and optional tags, along with the idempotent behavior tied to the topic key.

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 states the verb 'store' and the resource 'lesson' with clear scope 'across ALL your projects (cross-project knowledge)'. It distinguishes from sibling tools like learn_from_attempts (project-specific) and team_learn (team sharing), making the purpose unambiguous.

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?

Explicit guidance is provided: 'Use for tool preferences, personal workflows, platform quirks, and universal gotchas.' It also directs when not to use: '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.