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Met Obs

met_obs
Read-onlyIdempotent

Fetch meteorological observations from a NOAA station over a date range for a specific product (wind, air_temperature, water_temperature, air_pressure, humidity, conductivity, or visibility). Returns timestamped sensor readings.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
unitsNo
productYeswind | air_temperature | water_temperature | air_pressure | humidity | conductivity | visibility
stationYes
end_dateYes
time_zoneNo
begin_dateYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the date range scope and product constraint, plus the return format ('timestamped sensor readings'), but does not disclose date formatting, units semantics, or station ID requirements. With annotations in place, this is adequate but not exceptional.

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?

A single, front-loaded sentence states the action, resource, scope, and product list, then adds a brief return-value note. No wasted words or irrelevant details.

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

Completeness3/5

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

Output schema and annotations cover return values and safety. The description captures the core purpose but omits details on optional parameters (units, time_zone) and parameter formats, which are relevant for correct invocation. Adequate for a simple fetch tool, but with gaps for full agent self-sufficiency.

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

Parameters2/5

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

Schema coverage is only 17% (product has an enum description). The description adds the product list and implies date range, but does not explain station ID format, begin/end date format, units options, or time_zone semantics. Low schema coverage demands more compensation than provided.

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 uses a specific verb 'Fetch' with a clear resource 'meteorological observations from a NOAA station' and scope 'over a date range for a specific product'. It lists all product options, distinguishing it from sibling tools like water_level or currents that retrieve different data types.

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

Usage Guidelines3/5

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

Usage context is implied by the description: use when you need meteorological observations for a station/date/product. However, it does not explicitly mention when not to use it or name alternative tools, relying on the agent to infer from the product list and sibling names.

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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TDQS

B3.1/5.0
Disambiguation2/5

The 7 NOAA-specific tools (stations, station_metadata, water_level, currents, met_obs, predictions, datums) are clearly distinct, but they are buried among ~31 Pipeworx platform tools with heavy internal overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route factual queries, while polymarket_edges, polymarket_edge_tracker, polymarket_arbitrage, polymarket_fill_risk, and polymarket_kalshi_spread all analyze prediction-market opportunities. An agent cannot easily tell whether the generic question-answering or prediction-market tools are the right choice without reading long descriptions.

Naming Consistency2/5

Most tools use snake_case, but the naming conventions are inconsistent: some use descriptive nouns (stations, datums, predictions), some use noun_verb pairs (water_level, met_obs), and the Pipeworx batch mixes vendor-prefixed names (pipeworx_feedback, pipeworx_trending), bare verbs (remember, forget, recall, subscribe, unsubscribe), and multi-word verbs (generate_llms_txt, scan_competitor_ai_presence, ask_pipeworx_grounded). No predictable pattern unifies the set.

Tool Count1/5

38 tools is far too many for a server named 'Noaa Tides' — only 7 tools relate to NOAA tide/current data, and the other 31 are an unrelated general-purpose data platform (SEC filings, prediction markets, npm packages, AI visibility scanning, memory storage). The overwhelming majority of the surface has nothing to do with the server's stated purpose, making the count and composition a severe mismatch.

Completeness4/5

For the nominal NOAA tides domain, the surface is reasonably complete: station listing, metadata, observed water levels, currents, meteorological observations, tide predictions, and datums cover the core workflows. Minor gaps exist (e.g., no harmonic constituents or extreme water-level statistics tool), but the essential operations are present. The unrelated tools do not fill gaps in the NOAA domain — they are clutter rather than coverage.