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

The description adds significant behavioral context beyond the annotations: it emphasizes that the tool is 'Descriptive, not a market prediction, not advisory,' that it ships no directional forecast, and that only preregistered, out-of-sample-validated model tiers are used. These disclosures enrich the read-only/idempotent hints already provided by annotations without contradiction.

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 dense and front-loaded with the core purpose, then details outputs, caveats, and an alternative. While long, every sentence contributes necessary information (proxies list, outputs, validation, non-forecast disclaimer, sibling pointer). Its structure is logical, moving from overview to specifics to exclusions.

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 complex tool with 18 proxies and multiple outputs, the description is highly complete: it lists all output categories, explains the model tier selection, explicitly states what the tool does NOT provide (directional forecast), and points to a sibling tool for alternative needs. The existence of an output schema means return-value details need not be spelled out, but the description still covers operational context thoroughly.

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% (horizon_days described as 'Validated horizons only: 5 or 21 trading days'). The description reinforces the 5/21 horizon and adds meaning by explaining that per (asset, horizon) cells select model tiers (covariate logit / persistence / unconditional) and that probabilities are model-conditional over the chosen horizon. This contextualizes the parameter beyond the schema's basic constraint.

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 precisely states the tool's function: 'Compressed cross-category map of the current market state in ONE call' and enumerates the 18 category proxies and outputs (regime labels, probabilities, stress probability, forward-return distribution, commonality flag). It also distinguishes from the sibling tool regime_outlook by noting its single-asset depth with as_of support.

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 explicit guidance on when to use this tool versus alternatives: 'Use regime_outlook for single-asset depth with as_of support.' It also states a clear non-goal: 'Deliberately ships NO directional up/down forecast,' which sets expectations and helps the agent decide between this and other tools.

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