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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds meaningful behavioral context by disclosing the aggregation logic (top 35 most-active filers) and the exact output fields (trade count, distinct member count, purchase-vs-sale split, estimated disclosed volume range, party breakdown). This goes beyond the annotations and helps the agent understand what to expect without being contradictory.

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 three sentences: purpose, output summary, and usage examples. It is front-loaded with the core purpose, followed by a concise list of returned fields, and ends with practical example queries. Every sentence earns its place, and the structure is clean and efficient without excess verbiage.

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 the low complexity (2 optional parameters, no output schema), the description is complete: it explains the aggregation scope, lists return fields, and provides example usage. It does not describe the exact meaning of 'estimated disclosed volume range' but that is a field detail not essential for calling the tool. Overall, the description covers what an agent needs to invoke it correctly and interpret results.

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 input schema provides 100% coverage for both parameters (days and limit) with clear descriptions. The description mentions 'in the last N days' aligning with the days parameter, but does not add further semantic detail beyond the schema. Since schema coverage is full, the baseline of 3 is appropriate; the description does not introduce new parameter meaning, but it reinforces usage through example queries.

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 the tool returns tickers with the most congressional trading activity over a look-back window, aggregated across the top 35 most-active filers. It specifies the resource (tickers) and the specific aggregation scope, and the mention of 'top movers' in the title and the aggregated nature distinguishes it from sibling tools like politician_recent_trades or politician_member_activity. The purpose is unambiguous and specific.

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 example use cases ('what stocks is Congress most active in this week', 'are Democrats and Republicans trading the same names', 'biggest moves on the Hill this month'), which clearly indicate when to use this tool. It does not explicitly name alternatives or state when not to use it, but the context is strong enough for an agent to select it appropriately among siblings. The aggregation across top filers implies it is for aggregate trends rather than individual trade details, which is a useful usage hint.

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