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

With no annotations supplied, the description must carry the behavioral burden, and it does disclose two non-obvious traits: the cost model ("Free") relative to paid siblings and that the response reports data freshness. The small_sample flag is also a useful statistical caveat. It does not cover auth, rate limits, or whether the payload is cached, keeping it out of the top band.

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?

Two terse sentences, front-loaded with "Free.", then the exact payload contents and the call-first directive. No filler; the parenthetical enumerates return fields efficiently.

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?

No output schema exists, so the description is responsible for previewing the return, and it does list the key fields (closed trades, win rate, live days, small_sample, open book, freshness). Nothing an agent needs to call a parameterless getter is missing, though it does not describe the structure or shape of those values.

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 tool takes zero parameters, so the schema has nothing to explain and the baseline of 4 applies. The description correctly implies a no-argument call.

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?

States concretely what it returns: bot statistics (closed trades, win rate, live days, small_sample), an open-book summary, and data freshness. This is a specific verb+resource, though it never names or excludes the similarly-shaped siblings get_signals, get_history, or get_sample, so the agent must infer the boundary itself.

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?

"Read this before paying." gives a clear conditional for using this tool first, relative to paid siblings implied in the ecosystem. It stops short of naming any alternative tool or an explicit when-not-to-use case, so it is context without full routing.

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