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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=true, so the safety profile is covered; the description goes further by describing what comes back — original dated rows plus per-category purchase/sale, holdings-change, commentary and derivative directions — and flags the interpretive caveat that awards are non-directional. That is meaningful behavioral context beyond structured fields, not a restatement of the 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?

Three sentences, front-loaded with the core purpose and then the return contents, with no filler. The phrasing is dense and jargon-heavy ("bounded ... previews," "Awards are non-directional"), and the negation list at the end is long, but each clause carries real disambiguating information.

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 read-only annotations supplied, the description covers the remaining burden well: scope, data provenance (public previews), and the boundaries of the analysis. It stops short of explaining what "overlap" concretely means across the six categories or how conflicts are represented, leaving a small interpretive gap for a tool whose name promises judgment.

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?

There is a single parameter and schema description coverage is 100%, so the schema already documents the ticker pattern and its format. The description adds only the mild clarification that the ticker must be a US stock, which is roughly aligned with the regex but adds little syntax or constraint detail. Baseline 3 is appropriate when the schema does the heavy lifting.

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?

Uses a specific verb ("Inspect") on a specific resource ("agreement and conflict in the six bounded AlphaYou public smart money previews") scoped to "one US stock." It then explicitly fences itself off from adjacent capabilities (market-wide confluence scanner, distinct-actor count, scored signal, paid-call leak, saved alert), so an agent can distinguish it from get_smart_money and other siblings without opening a schema.

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 negative guidance is unusually explicit: it tells the agent when this tool is NOT the right choice (not a scanner, not a scored signal, not a saved alert, not a paid-call leak), which effectively routes those intents elsewhere. What is missing is a positive statement of the triggering situation (e.g., "use when comparing a single ticker's aggregate smart-money positioning") and any named alternative.

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