check_feature_drift

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

No annotations are provided, so the description carries the full burden. It usefully discloses that inputs may be public HTTP/HTTPS URLs (network access) as well as local paths, but says nothing about the return value (p-value, statistic, verdict), sampling behavior, or failure modes for non-numeric features.

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?

Two tight sentences, front-loaded with the operation and method, followed by the input-source caveat. No 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?

With no annotations, no output schema, and 0% schema description coverage, the description should explain what a caller receives (test statistic, p-value, pass/fail) and the role of significance_level. Neither appears, leaving the tool under-specified for its complexity.

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 schema alone documents nothing. The description partially compensates by explaining that baseline_csv/current_csv can be paths or URLs and that the feature must be continuous, but significance_level and the exact meaning of feature_column go unaddressed.

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 (evaluates), a precise statistical target (covariate shift for a continuous feature), the datasets involved, and the exact test method (Kolmogorov-Smirnov). That is far more specific than the sibling names alone, though it never explicitly distinguishes itself from compute_psi, which is another drift metric.

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?

There is no statement of when to prefer this over compute_psi or the monitoring-policy tools, and no mention of prerequisites such as minimum sample size or the data types the KS test requires. Usage context is only inferable from the word 'drift'.

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