get_lifetime_energy

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds the important temporal behavior—cumulative total from meter activation to the current date—which is useful context beyond the annotations and helps avoid misinterating the query with a daily or period-specific measurement.

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 entire description is a single, focused sentence in Chinese with no redundant phrasing. It front-loads the action and immediately specifies the exact time horizon, so every part of the sentence adds value.

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 parameterless, read-only, idempotent tool with an output schema and clear annotations, this description provides all essential operational context. The agent knows what action to take, what the scope is, and that no side effects or required arguments exist.

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 are zero parameters and schema description coverage is 100%, so the description has no parameter burden. The baseline of 4 for parameterless tools is appropriate; there are no parameters whose meaning needs clarification.

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 names a specific verb ('读取' meaning 'read') and a precise resource: the meter's cumulative total electricity consumption from online activation through the current date. This clearly differentiates it from siblings like get_daily_energy which implies a daily scope.

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 lifetime/date-range semantics are explicit, so an agent can infer this is appropriate when cumulative since-online data is needed. It does not explicitly mention alternatives or exclusions, but the scope language effectively routes around daily or summary siblings.

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