learning_outcome_stats

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

Discloses non-obvious behavior: aggregates are background-computed and retrieved from memory, not real-time. No annotations to contradict.

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?

Two concise paragraphs front-loading purpose and detailing return values. Every sentence adds value.

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?

Covers purpose, return values (outcome counts, groupings, coverage, timestamps), behavior (background computation, memory retrieval), and use case. No gaps given zero params and no output schema.

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?

No parameters; schema coverage is 100% trivially. Baseline 4 for zero-param tools. Description adds no param info but none needed.

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 action: 'Get outcome distribution snapshot over 1-hour recent window grouped by tier and model.' Distinguishes from siblings like record_outcome and inspect_status.

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 recommends use for 'operator observability into routing quality by model.' Lacks explicit when-not-to-use but context implies this is the snapshot tool vs recording tools.

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