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

Annotations already state readOnlyHint, idempotentHint, and destructiveHint=false, so the bar is lower. The description adds behavioral detail: chronological high/low output, UTC timestamps, MLLW datum default, harmonic prediction generation, and no-auth public domain status. No contradiction with annotations.

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 what it returns, and subsequent sentences cover defaults, datum, use cases, source, and auth. It avoids redundant restatement of parameter details despite being longer than the bare minimum.

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 full schema coverage, an output schema, and safety annotations, the description is effectively complete. It communicates scope, output shape, defaults, station ID examples, datum semantics, source, and auth requirements. An agent can confidently select and invoke this tool without needing additional context.

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 input schema covers all parameters at 100%, so the baseline is 3, but the description adds useful examples like station IDs for Boston and San Francisco, the default 7-day range, and the MLLW datum context. This exceeds what the schema alone provides.

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 is specific: it says the tool gets predicted high/low tide times and heights for NOAA CO-OPS stations, and describes the result shape (chronological H/L list with UTC timestamps and water heights). It does not distinguish itself explicitly from sibling tools like coops.tides.water_level or tides.noaa.predictions beyond the high/low framing.

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?

It gives practical context for when to use the predictions (sailing, fishing, coastal engineering, flood planning) and notes the data is harmonic predictions, which helps an agent decide if this tool is appropriate. It does not explicitly state when to use an alternative like water_level, so the when-not-to-use guidance is left to inference.

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