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

Annotations already mark it as read-only and open-world, but the description adds useful behavioral context: it is free, requires no payment, uses SIC-based ranking, and is scoped to US companies. It still omits details like rate limits or behavior when no peers match.

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 short and front-loaded, but the bracketed suffix repeats 'falcon.sol', 'comps', and 'Free' without adding much new information. It is usable but contains redundancy rather than tight economy.

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?

For a simple two-parameter suggestion tool with no output schema, the description covers the core purpose, ranking method, and downstream use. However, it leaves gaps around limit behavior, ticker format, and expected result shape that an agent would need to infer.

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 only indirectly clarifies that ticker refers to a US company and says nothing about the optional limit parameter, leaving parameter meaning largely undocumented.

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 suggests peer tickers for a US company using SIC classification and revenue closeness ranking. It also explicitly names get_comps as the downstream consumer, which helps differentiate it from that sibling.

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 states the intended workflow: get peer tickers, then pass them to get_comps. It also clarifies the free/no-payment context. However, it does not explicitly say when not to use it versus other comps-related siblings like get_gpu_comps.

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