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

No annotations are provided, so the description carries the full burden. It discloses the return payload structure ('5-layer payload + `degraded:bool` + `missing_layers:[...]`'), partial failure behavior ('partial failures still produce usable output'), and caching semantics ('Cache TTL 60s. Pass `cache_max_age_s=0` to bypass cache'). This is transparent beyond typical descriptions.

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?

Each sentence carries unique, high-value information: purpose, usage, benefit, return details, degradation handling, and cache control. The list of five collapsed tools is relevant for disambiguation. No filler or redundancy.

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?

Given the tool's complexity and lack of output schema, the description provides a strong high-level contract: what it aggregates, returns, partial failure behavior, and cache semantics. It does not detail the internal structure of each of the five layers, but the names and the overall summary are sufficient for an agent to decide and invoke correctly.

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 description coverage is 50% (only `ticker` is described). The description adds valuable semantics for `cache_max_age_s` by explaining the bypass-cache behavior, which the schema does not. `ticker` is self-explanatory and already documented, so the addition is meaningful.

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 a specific action ('collapses'/'aggregation') with a clear resource ('ML-transparency aggregation for one ticker') and explicitly names the five underlying tools, distinguishing it from sibling tools. The use case ('why is the model saying this?') further clarifies intent.

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?

Provides an explicit when-to-use: 'Use when Brain's verdict needs the model-transparency layer' and quantifies the benefit ('Saves 4 HTTP calls per verdict'). It lists the collapsed tools as a form of alternative but does not explicitly say when not to use this aggregate vs. individual tools.

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