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Get Water Level Summaries (High/Low, Daily, Monthly)

noaa_get_water_level_summaries
Read-onlyIdempotent

Get verified summary water-level products from a NOAA station:

  • high_low: each day's observed highs/lows with ty = HH (higher high), H, L, LL (lower low). Max 1 year per request.

  • daily_mean: daily mean levels — GREAT LAKES STATIONS ONLY; NOAA requires local standard time, which this tool applies automatically. Max 10 years.

  • daily_max_min: daily maxima/minima from hourly and 6-minute data with completeness percentages. Max 10 years.

  • monthly_mean: monthly tidal datum means (columns MHHW, MHW, MSL, MTL, MLW, MLLW, DTL, GT, MN, DHQ, DLQ, HWI, LWI, highest, lowest). Max 200 years — ideal for long-term climatology.

Use for: historical extremes, mixed-tide analysis (HH vs H), long-term averages. For raw time series use noaa_get_water_levels; for future tides use noaa_get_tide_predictions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoShortcut window: "today" = midnight to now, "latest" = single most recent reading, "recent" = last 72 hours. Mutually exclusive with begin_date/end_date/range.
datumNoVertical reference datum for heights. MLLW is the standard chart datum for coastal stations. IGLD and LWD apply to Great Lakes stations ONLY; NAVD/CRD exist only at stations where computed. Check a station's supported datums with noaa_get_station_datums.MLLW
rangeNoNumber of hours. With begin_date: hours forward. With end_date: hours back. Alone: hours back from now.
unitsNoUnit system. english: feet, °F, knots (wind AND currents), nautical miles. metric: meters, °C, m/s for wind but cm/s for currents, kilometers. Air pressure is millibars and salinity is PSU in BOTH systems.english
productYesSummary product: high_low (daily tide extremes, 1yr max), daily_mean (Great Lakes only, 10yr), daily_max_min (10yr), monthly_mean (datum means, 200yr).
stationYesStation ID. Water-level/met stations use 7-digit numeric IDs (e.g. "9414290" San Francisco); current stations use alphanumeric IDs (e.g. "cb0102"). Find stations with noaa_search_stations or noaa_find_nearest_stations.
end_dateNoEnd date/time. Same formats as begin_date.
time_zoneNoTime zone for timestamps: gmt = UTC, lst = station local standard time (no DST), lst_ldt = station local time with DST. Note: daily_mean data requires lst.lst_ldt
begin_dateNoStart date/time. Formats: yyyyMMdd, "yyyyMMdd HH:mm", MM/dd/yyyy, or ISO yyyy-MM-dd[THH:mm].
response_formatNoOutput format: "markdown" for a readable summary table, "json" for the complete structured payload.markdown

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds critical behavioral context: per-product request limits (1 yr for high_low, 10 yr for daily_mean/daily_max_min, 200 yr for monthly_mean), the Great Lakes-only constraint for daily_mean, and automatic application of local standard time. No contradictions 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.

Conciseness5/5

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

The description is efficiently structured: a lead sentence, bullet points for each product, and a concise usage line at the end. Every sentence adds value without redundancy. It is about 7 lines, front-loading key actions and constraints.

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

Completeness5/5

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

Given the complexity (10 parameters, no output schema), the description is remarkably complete. It covers all product types with time limits, station applicability, datum nuances, and references to sibling tools for alternatives and supporting info (e.g., noaa_get_station_datums for supported datums). Handles edge cases like Great Lakes-only and mandatory mode for daily_mean.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. However, the description adds significant value beyond schema: it explains each product option in detail, caveats for datum types (e.g., IGLD/LWD Great Lakes only, NAVD/CRD only where computed), and clarifies that daily_mean requires lst which the tool handles automatically. This elevates the score.

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 summary water-level products from NOAA stations, listing four specific product types with detailed explanations (high_low, daily_mean, daily_max_min, monthly_mean). It distinguishes from sibling tools by specifying when to use noaa_get_water_levels and noaa_get_tide_predictions instead.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: 'Use for: historical extremes, mixed-tide analysis (HH vs H), long-term averages.' It also directs users to alternatives: 'For raw time series use noaa_get_water_levels; for future tides use noaa_get_tide_predictions.' This clearly defines when and when not to use the tool.

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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