get_learning_metrics

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

With no annotations, the description discloses that calls require a specific scope, are governed by constitutional policy, and produce a ProofLink receipt. This adds valuable context beyond the name, but it doesn't specify the return format, whether the operation is purely read-only, or any side effects beyond the receipt.

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 two concise sentences with no filler. The first sentence states the purpose and metrics, the second adds scope and governance context, making it efficient and front-loaded.

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 zero-parameter tool with no output schema, the description covers the purpose, required scope, and unique behavioral traits (policy and receipt), and lists the metrics returned. It could elaborate on the response structure or what constitutes a 'cycle,' but these are minor gaps for tool selection.

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 has zero parameters, so the description correctly avoids adding parameter details. Per the rubric, zero parameters gives a baseline of 4, and the description does not need to compensate for schema shortcomings.

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 retrieves Learning Arbiter cycle metrics and enumerates the specific metric types (processed, promoted, rejected, known patterns). It is distinct from sibling tools such as get_learning_queue, which focuses on queue items rather than cycle-level metrics.

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 cycle metrics are needed and states a scope requirement, but it does not explicitly contrast with alternatives or provide when-not-to-use guidance. It relies on the tool name and first sentence to convey context, making usage mostly implicit.

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