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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the output is AI-generated and changes meaning based on currency presence. However, it does not mention data freshness, response format, rate limits, or whether the analysis may be non-deterministic, which are relevant for an AI-generated tool.

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 remarkably concise: two sentences with no filler. The core purpose is front-loaded, the conditional currency behavior is clearly stated, and the language parameter is enumerated compactly. Every sentence contributes useful information.

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 low-complexity tool with two optional parameters and no output schema, the description provides enough context to invoke it correctly: what it does, how currency changes behavior, and which languages are supported. A more explicit statement of output type or freshness would be helpful, but the AI-generated analysis wording makes the expected output reasonably clear.

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 coverage is 100%, so the baseline is 3. The description adds meaningful semantic value by explaining that omitting currency produces a whole-market summary, which the schema does not state. It also reiterates the lang enum values. This goes beyond a simple restatement of the schema.

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's function: an AI-generated analysis of the Uruguayan exchange market, with a whole-market summary by default and per-currency analysis when a currency is supplied. It is specific about the domain and the conditional behavior. However, it does not explicitly distinguish itself from sibling tools such as get_rates or get_evolution.

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 clear conditional usage: use without currency for a whole-market summary, with currency for per-currency analysis. It also specifies supported language values. However, it gives no direct guidance on when to choose daily_summary over sibling tools like get_rates, get_news, or get_evolution, leaving the selection to inference.

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