Price reaction around an event

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it clarifies that the computation is 'pure arithmetic on price data' and explicitly disclaims causal interpretation. It also discloses the after-close publication mapping behavior in the disclosure_id parameter description, which is a non-obvious behavioral detail.

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 two sentences with zero waste. The first sentence front-loads the core output and scope, and the second sentence adds a crucial caveat about causation. Every word earns its place.

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 tool has an output schema, so return values need not be described in detail. The description covers the core data, the event/disclosure input modes, and the non-causal nature of the computation. The only minor gap is that it does not explicitly state when to prefer this over get_price_history or get_company_timeline, but the event-centric framing and disclosure_id parameter make the use case clear enough.

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 60%, and the description adds value by explaining the event-date/disclosure_id duality and the after-close mapping behavior. The main description also clarifies that the output includes excess return versus BIST 100, which gives meaning to the tool's purpose. However, days_before and days_after semantics are only partially explained by the schema defaults and bounds, and the description does not elaborate on them further.

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 verb ('get') and resource ('price reaction around an event date'), and enumerates the exact data returned: daily closes, returns, volume, and excess return versus BIST 100. It also distinguishes itself from a causal analysis tool by explicitly disclaiming causation, which helps an agent understand what this tool is not.

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 clearly implies when to use this tool: when an agent needs price/volume behavior around an event date or disclosure, and it explicitly notes the tool does not assert causation, which is a useful exclusion. It does not name sibling alternatives like get_price_history or get_company_timeline, but the event-centric framing and the disclosure_id parameter provide enough context for an agent to select it.

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