approve_learning_item

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

With no annotations, the description carries the full burden. It discloses the required scope (learning:arbiter:write), the governance by Arbiter constitutional policy, and the ProofLink receipt, which is valuable. However, it does not mention what happens after approval, potential side effects, or what the response contains.

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 compact, uses two short paragraphs, and every sentence adds key information: purpose, required parameter, scope, and receipt behavior. There is no redundancy or filler.

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 simple one-parameter tool, the description covers the core purpose, required input, and important security/governance context. It could mention the return value or receipt specifics, but the absence of an output schema and the low complexity make this adequate.

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 already provides 100% coverage of the single parameter, including its origin ('Pattern ID from get_learning_queue'). The description merely repeats 'requires pattern_id' without adding extra semantic detail like format or constraints, so it stays at the schema-driven baseline.

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 specific action ('Approve a Tier 2 learning pattern') and the target resource ('promotion to Agent Brain'). It distinguishes this from sibling tools like approve_sie_finding by identifying the unique resource type (Tier 2 learning pattern).

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 provides clear context on when to use it, specifically for approving Tier 2 learning patterns, and names the required parameter pattern_id. It does not explicitly mention alternatives or exclusions, but the resource specificity makes the intended use unambiguous.

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