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cyntrica

Gov Data MCP

by cyntrica

coops_water_level

Read-only

Retrieve observed water levels from NOAA CO-OPS stations using a station ID and date range or shortcuts for today, latest, or recent.

Instructions

Get observed water levels from a NOAA CO-OPS station. Provide station ID + date range (YYYYMMDD) or date shortcut (today/latest/recent).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoDate shortcut (alternative to begin_date/end_date)
datumNoVertical datum (default STND)
unitsNoUnits: english or metric (default metric)
stationYesStation ID, e.g. '8454000' (Providence), '9414290' (San Francisco)
end_dateNoEnd date YYYYMMDD
time_zoneNoTime zone: gmt, lst (local standard), lst_ldt (local daylight) (default gmt)
begin_dateNoStart date YYYYMMDD
Behavior3/5

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

Annotations declare readOnlyHint=true, and the description's 'Get' aligns with that. The description adds minimal behavioral context beyond the annotations, such as the date format and shortcuts, but these are already present in the schema. It doesn't disclose limitations, default units, or the nature of the returned data, so it adds little beyond the structured metadata.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is exceptionally concise, two sentences that immediately convey the purpose and the minimal required inputs. It is front-loaded with the action and resource, and every clause serves a functional role without any redundancy or boilerplate.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with a well-documented schema, the description is nearly sufficient. It covers the essential inputs (station and date) and the distinction from predicted data. However, it does not mention the optional parameters or default behaviors (e.g., default units), leaving some context to the schema. Still, the schema is rich enough to compensate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all parameters are already documented. The description essentially restates the station and date requirements without adding new meaning. It reinforces that station is required and that dates can be a range or shortcut, but this is already captured in the schema's descriptions and enums, so it provides no additional value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves observed water levels from a NOAA CO-OPS station, using the specific verb 'Get' and identifying the resource and source. It distinguishes itself from sibling tools like coops_tide_predictions and coops_high_low by emphasizing 'observed' water levels, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context on how to invoke the tool (provide station ID and date range or shortcut) but does not explicitly name alternative tools for predicted levels or when not to use this tool. The 'observed water levels' wording implies a contrast with prediction tools, but lacks explicit exclusions or alternative recommendations.

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

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