arnfa_ask

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

With no annotations, the description carries the burden and does well: it states the tool reads live weather data, returns a plan, and explains the follow-up mechanism using prior. It doesn't cover error behavior or rate limits, but for a query tool, the core behavior is transparent enough.

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 sentences, front-loaded with the core purpose, and every clause earns its place. It avoids fluff and clearly explains the main action and the follow-up pattern.

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-param tool with no output schema or annotations, the description is complete: it specifies language, input type, return value, and chaining pattern. It provides enough context for an agent to select and invoke the tool correctly.

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 value by explaining how to use 'prior' in follow-up calls, linking it to the previous reply's 'intent'. This clarifies the chaining behavior beyond what the schema states.

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 uses a specific verb ('Ask') and resource ('Arnfa AI') and clearly states the outcome: a weather-fit plan based on real sky data. It distinguishes itself from siblings by emphasizing natural Thai/English input and the 'same grounded engine, narrated', which differentiates it from more structured tools like arnfa_plan_day.

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 gives clear context for when to use the tool (natural language queries in Thai/English, follow-up refinements) but does not explicitly name alternatives or exclusions. The phrase 'the same grounded engine' implies a relationship to arnfa_plan_day, but an explicit comparison is missing.

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