compare_to_observed

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

With no annotations, the description carries the transparency burden. It usefully discloses blank-cell handling, per-key statistics, and an inline overlay plot. However, it does not say whether output_path writes files, whether plot=false suppresses output, or what other side effects occur.

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: the first sentence states the purpose and metrics, and the second paragraph adds the essential CSV contract. There is no filler or redundancy.

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?

Without an output schema, the description should fully define the call contract. It gives a good account of the observed CSV format and return shape at a high level, but three of five parameters are unexplained and the exact stats/plot behavior is underspecified for a no-annotation tool.

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 0%, so the description must explain the parameters. It clarifies observed_csv's expected structure and the meaning of keys, but leaves case_path, plot, and output_path undefined, which is a significant gap for a five-parameter tool.

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 operation (history-match QC) and the exact metrics (RMSE/bias/MAE/NRMSE) over simulated vs observed data, which is clear. It does not explicitly differentiate this from siblings like compare_summaries or plot_summary, so it stops 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?

The intended use is implied by 'History-match QC' and the observed-data CSV format, but the description never states when to prefer this tool over alternatives such as compare_summaries, nor does it give exclusions or prerequisites.

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