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

The annotations already indicate a safe, read-only, non-destructive operation, so less descriptive burden is placed on the tool. The description adds useful behavioral context: requests are geographically targeted from a chosen country/state and the response includes cited sources, which is not inferable from 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?

The description is compact: two sentences with no filler. It front-loads the primary action and result, then adds a focused use-case sentence that intrinsically justifies the tool.

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 core purpose, security profile, and return value are covered. The nested include flags and helper tools list_countries/list_states are documented in the schema rather than the description, which is acceptable given 100% schema coverage and read-only annotations.

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 has 100% coverage with clear descriptions for all four parameters. The description adds no parameter-specific guidance beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 names a specific verb (submit a prompt) and resource (Microsoft Copilot) and adds distinguishing scope: geo-targeting by country/state and returning cited sources. It clearly separates this tool from sibling scrapers by model and from list_countries/list_states by focusing on actual generation.

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 a concrete use case: 'Use this to see how Microsoft Copilot answers a prompt and which brands/sources it mentions.' It does not explicitly say when not to use it or name alternatives like scrape_chatgpt, but the intended context is clear.

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