get_variable_stats

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

With no annotations provided, the description carries the full burden of explaining behavior. It states that the tool computes a statistical summary and returns a JSON string, which is the core functional behavior. However, it does not explicitly note that the operation is read-only, how it handles missing or non-numeric variables, or whether large datasets could be expensive to process.

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 compact and front-loaded, starting with a clear one-sentence purpose and then listing parameters and return type. Every line serves a purpose, and the Args/Returns structure makes the content easy to scan without redundant filler.

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 two-parameter read-only tool that has an output schema, the description supplies the essential purpose, parameter meanings, and return type. The only notable gap is the absence of explicit sibling-tool differentiation or a note about when this tool should be used versus related analysis tools.

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 schema has 0% description coverage, so the description must compensate. It does so minimally by explaining file_path as a path to a NetCDF file and variable_name as the variable to analyze. This adds meaning beyond the bare schema titles, but it lacks detail about constraints such as requiring a numeric variable or the variable needing to exist in the file.

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 clearly states a specific verb and resource: it gets a statistical summary of a variable, and enumerates the exact statistics (min, max, mean, std, NaN count, percentiles). This distinguishes it from sibling tools like get_variable_info or get_variable_sample, which focus on metadata or raw sample values.

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 when a statistical summary is needed, but it does not explicitly say when to prefer this tool over alternatives such as get_variable_info or get_variable_sample. There is no comparative guidance or exclusion criteria, leaving the agent to infer the appropriate context from the purpose.

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