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

Beyond readOnlyHint/idempotentHint, description discloses that seasonality was tested and falsified, that only the preregistered out-of-sample-validated tier ships, and that baselines are included for comparison. It also includes a disclaimer that probabilities are descriptive, not a market prediction or advisory. No contradiction with annotations.

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 long but information-dense, with the core purpose in the first sentence. Each clause earns its place, though it could be split into shorter sentences for easier parsing.

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 an output schema exists, the description is complete: it covers scope, assets, horizons, validation status, baseline reporting, and a clear disclaimer. Addresses complexity of model-conditional probabilities well.

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% and each parameter already has a description. The description adds context: as_of computes a historical outlook, horizon days are validated only 5 or 21, and asset is restricted to the validated list. It enriches the semantic meaning of the output (probability complement) beyond the schema.

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 opens with a specific verb phrase 'Model-conditional probabilities that an asset is in each market regime' and names the exact output (BULL/SIDEWAYS/BEAR/CRISIS probabilities) and horizon. It explicitly contrasts with sibling stress tools ('probability complement'), distinguishing it from run_stress_test/portfolio_stress_test.

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?

States when to use this vs stress tools: 'stress tools answer what happens GIVEN regime X, this answers how likely is regime X from today's observable state'. Also constrains usage to validated assets and horizons, and mentions baselines for calibration. No explicit when-not but the complement framing is clear.

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