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

Annotations already declare readOnlyHint=true, and the description adds meaningful behavioral context by naming the underlying data sources (FINRA bi-monthly short interest, flow-based retail social sentiment) and explaining what each series includes. It does not contradict annotations and gives enough transparency beyond the schema.

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 well-structured: a one-line purpose, a bulleted breakdown of the two `kind` options with aliases and sources, and a closing routing note. Every sentence earns its place, and the most important selection logic is front-loaded.

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?

With an output schema present and the safety profile covered by readOnlyHint=true, the description provides enough context for an agent to select the correct kind and avoid nearby sibling tools. The only notable gap is the lack of explanation for `entity` and `limit`, but the purpose and data sources are otherwise sufficiently described.

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 0%, so the description must compensate. It thoroughly explains the `kind` parameter and its aliases, but it does not explain `entity` or `limit` beyond the schema's bare type/default information. The partial compensation for one parameter while leaving two undocumented keeps this at a mid score.

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 clearly states that the tool provides positioning and sentiment signals on a stock and then concretely enumerates the two kinds (short_interest and sentiment) with their aliases. It also distinguishes itself from relevant siblings by explicitly routing manager holdings to get_ownership and insider-buying screens to get_insider_window.

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?

It explicitly tells the agent how to select behavior via `kind`, lists all aliases, and provides direct alternatives for adjacent use cases ('For a manager's holdings use get_ownership; for the market-wide insider-buying screen use get_insider_window.'). This is exactly the kind of when-to-use guidance needed.

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