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

With no annotations, the description must carry the behavioral burden. It usefully discloses data provenance (free dual-source, NASA POWER 30-year baseline, 7-day forecast) and fallback behavior for arbitrary Chinese place names, but says nothing about read-only nature, rate limits, auth, or failure modes.

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?

A single dense sentence, front-loaded with the verb and the data types the tool returns, with detail tucked into one trailing parenthetical. Efficient, though the parenthetical is information-packed.

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?

There is no output schema and no annotations, so the description should describe more about the returned structure (how real-time, forecast, and baseline are organized). It covers sources and coverage but leaves the response shape unspecified.

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 schema already documents both parameters including defaults. The description adds only marginal meaning (the arbitrary-place-name fallback to southern Sichuan coordinates), which is correct at the 3 baseline.

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?

States a specific verb (获取) and a rich, well-scoped resource set: real-time weather + 7-day forecast + NASA POWER 30-year climate baseline for agricultural regions. The resource type (weather data) inherently differs from the sibling suzi_weather_alerts, but the description never names or contrasts that sibling explicitly.

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 explains what data it returns and its coverage, but gives no when-to-use guidance and never mentions suzi_weather_alerts or any condition for choosing between them. The agent must infer usage from the resource itself.

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

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

No annotations are provided, so the description carries the full burden, and it does disclose meaningful behavior: the 7-day scan window, the four alert categories emitted, that each alert carries a level and a suggested action, and a candid reliability caveat that thresholds are uncalibrated inferred values. It omits any auth, rate-limit, or failure-mode context, keeping it below 5.

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?

One dense sentence front-loads the scan window and output, followed by a short caveat sentence. No filler, though the caveat could be tidier.

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 single-optional-param tool with no output schema and no annotations, the description adequately covers what is produced (categorized alerts with levels and actions) and flags data reliability. Only the lack of relational guidance versus suzi_weather keeps it from a 5.

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?

There is a single optional parameter (region) whose schema description is fully covered, including its 泸州 default, so the schema already does the work. The description adds nothing about region semantics; baseline 3 applies.

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?

States a specific verb (扫描) and resource (未来7天气象) and specifies the output artifact (农事预警事件 with 等级与建议动作), which implicitly separates it from the sibling suzi_weather's raw forecast. It does not explicitly name the sibling or contrast the two, so it falls short of a 5.

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?

Usage is only implied: an agent can infer this is for agricultural warning checks over the next week, but there is no explicit when-to-use statement, no exclusions, and no reference to suzi_weather as the plain-forecast alternative.

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