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

Annotations already declare this read-only, idempotent, and non-destructive. The description adds valuable context beyond those annotations: keyless public API, government public-domain data, automatic nearest-station selection, local station-time reporting, and standard deviation when reported. It does not cover failure modes or data latency, but that is not a major gap for this low-risk read tool.

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?

Three tightly written sentences front-load the action and source, then cover input modes, outputs, and intended use with no filler. Every sentence earns its place.

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 five-parameter, no-output-schema read tool, the description covers all the important operational facts: data source, input options, automatic station selection, default datum, and the returned fields. An agent has enough to select and invoke this tool correctly 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?

Schema coverage is 100%, so each parameter is already documented, and the description adds no new syntax details. It does reinforce the station-vs-lat/lon disjunction and the MLLW default, but those are also apparent from the schema.

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 precise verb and resource ('Return the latest observed water level from a NOAA Tides & Currents station') and identifies the source. It also distinguishes this tool from prediction tools by framing it as real-world observed data 'versus prediction', which separates it from the sibling tide_predictions.

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 clear context for when to use the tool ('check current real-world water level versus prediction') and specifies the two accepted input modes (station id or lat/lon). It does not explicitly state when not to use it or name the prediction sibling, but the use case is clear enough to route an agent.

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