stat_daily_avg

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 reveals a key non-obvious behavior: the server automatically converts data granularity based on the requested period. This goes beyond the schema and helps the agent anticipate output granularity. It does not cover response format or pagination, but the disclosed auto-conversion is significant and valuable.

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 with no filler. It front-loads the core purpose ('queries average data by period') followed by a concise, high-value clarification of the auto-conversion logic. Every word earns its place and the structure is easy to parse.

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?

The description covers the central behavior well, but the absence of an output schema or annotations leaves gaps: the agent does not know the shape of the returned data, whether maxRows interacts with the auto-conversion, or other response details. It is adequate for understanding the main logic but not complete enough for a fully informed call.

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 extra meaning to startDate and endDate by explaining how the date-range length determines the data granularity (raw, hourly average, daily average). This directly enhances understanding of the two key parameters beyond their simple type/format descriptions. Other parameters like maxRows and deviceId are not elaborated, but the schema already covers them well.

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 states a specific action: it queries average data over a period ('평균 데이터를 기간으로 조회한다'). It also explains the server's auto-conversion behavior, which adds clarity. However, it does not explicitly name or differentiate itself from sibling tools like data_raw_search or stat_weather, so it falls short of full distinction.

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 implies usage by explaining period-based granularity: raw for ≤1 day, hourly for ≤30 days, daily for >30 days. This gives context for choosing the tool based on date range, but it does not explicitly state when to use this vs. alternatives or provide exclusions. The guidance is implied, not direct.

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