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

Annotations already provide readOnlyHint=true. The description adds meaningful behavioral context beyond that: it is precomputed from a backtest, involves no model call, and is explicitly 'never a recommendation' but a historical record. This helps set correct expectations.

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 compact and front-loaded: it leads with the core ranking concept, then direction semantics, then usage triggers, then a caveat. Every sentence earns its place with no 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?

The description covers purpose, direction semantics, use cases, and important behavioral caveats, and an output schema exists. The only notable gap is the unexplained limit parameter, which prevents a perfect score.

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 description fully explains the direction parameter ('up' surfaces best historical forward returns, 'down' surfaces worst). However, the limit parameter is not mentioned at all, and schema description coverage is 0%, so the agent must guess what limit controls.

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: ranking the whole market by historical forward returns of each company's current condition set. It explicitly defines the up/down direction semantics, which clearly differentiates it from sibling tools like get_market or screen.

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 gives explicit user-query triggers: 'what looks good right now', 'what setups are strongest/weakest today', 'screen the market', and 'find high-probability situations'. However, it does not name alternative tools or state when not to use it, so it stops short of full comparative guidance.

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