AI market summary

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

With no annotations, the description carries the full disclosure burden. It states the output is 'AI-generated analysis,' implying synthesized narrative rather than raw data, and it exposes language options. It does not mention read-only semantics, data freshness, output format, or behavior when a requested currency has no data, so some behavioral gaps remain.

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 concise sentences with the core scope front-loaded. The conditional usage is compressed into one clear clause, and the language note is minimal. There is no filler or redundant phrasing.

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 tool with two optional parameters and no output schema, the description captures the essential invocation modes and market scope. It does not specify the return format beyond 'analysis,' and 'daily' is implied by the name rather than described, so it is not fully exhaustive. Still, an agent can call it correctly without requiring additional information.

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 100%, so the baseline is 3. The description adds the conditional semantics of the currency parameter ('without... with...') and restates the lang values, but the schema already conveys the optional per-currency behavior and language choices. The added value beyond the schema is minimal.

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 opens with 'AI-generated analysis of the Uruguayan exchange market,' clearly identifying the deliverable and market scope. It further differentiates the tool through its conditional behavior: whole-market without currency, per-currency with one. The verb is implicit rather than explicit, but the purpose is not likely to be confused with sibling rate or news tools.

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 explicitly explains how to switch between whole-market and per-currency modes: omit currency for a market summary, provide one for per-currency analysis. It also enumerates the allowed language values. However, it does not contrast this tool with siblings such as get_rates or get_evolution, leaving when to prefer raw rates or historical data to inference.

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