seasonality

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

Beyond the readOnly/openWorld annotations, the description reveals non-obvious behavior: the endpoint rejects requests without a year, back_year's meaning is unknown and passed through unchanged, and the response shape is unverified. It also clarifies that empty or null data means Stockbit has no series, not an error or a zero value.

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 longer than average, but it is dense with important warnings and caveats. The core purpose is front-loaded, and each paragraph addresses a distinct operational concern. It could be tightened slightly, but the length is justified by the amount of non-obvious context.

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?

With no output schema, the description explicitly states that the response shape is unverified, tells the agent to read field names from the raw data, and explains how to interpret empty/null data. Combined with the year and back_year guidance, the agent has everything needed to call and interpret this 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?

While the schema already documents all parameters, the description adds decisive semantics: year is the end of the ten-year lookback, not a count; omitting it sends the current year; back_year must not be interpreted as meaningful; and data is returned raw without renaming. These caveats materially change how an agent should set and interpret parameters.

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 first sentence clearly defines the tool's purpose: month-by-month seasonal price behavior for one IDX stock across a fixed ten-year lookback. It also explicitly disclaims being a forecast, which helps set expectations. However, it never names or contrasts sibling tools like technicals or patterns, so differentiation is left to inference.

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: use this when you need calendar-month seasonal price behavior for one IDX stock. It also provides useful exclusions, such as 'not a forecast' and 'says nothing about why a month was strong.' It lacks explicit 'use instead of X' routing to sibling tools, so it does not fully earn a 5.

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