dmm_get_statistics

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

The description discloses the core behavior: returning four summary statistics. However, with no annotations present, it does not state whether the statistics are computed live, whether statistics must be previously enabled, or what happens if no statistics are available.

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 a single concise sentence that front-loads the operation and the result data. No filler or redundant text is present.

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 zero-parameter query the description is mostly sufficient, especially with an output schema available to document the return shape. The main gap is the unstated requirement that statistics need to be enabled, but the sibling tool set makes this inferable.

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?

The tool has zero parameters, so the schema is trivially complete. Per the rubric, a zero-parameter tool earns a baseline of 4; the description does not need to add parameter meaning.

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 ('Query'), a specific resource ('statistics from the multimeter'), and enumerates the returned values (average, min, max, stddev). It clearly distinguishes this tool from the other DMM actions such as dmm_read, dmm_fetch, and dmm_measure.

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 no guidance on when to use this tool versus alternatives, and no mention of prerequisites such as statistics being enabled. Sibling names like dmm_enable_statistics imply a precondition, but the description itself does not communicate it.

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