station_metadataFetch full metadata for a single NOAA station by its numeric station ID (e.g. 8454000), including location, available products, and operational status.
| Name | Required | Description | Default |
|---|---|---|---|
| station | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 constraint that the ID must be numeric and the content categories, but it does not disclose additional behavior such as error responses, rate limits, or data freshness. Given the annotations, the description provides modest but acceptable value.
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 a single, front-loaded sentence that immediately states the action and resource. Every phrase contributes value: 'full metadata', 'numeric station ID', and the content list. There is no redundancy or filler.
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 only one parameter, an output schema present, and safe/idempotent annotations, the description covers all essential aspects for correct invocation. It explains the required input format and what to expect in the output (location, products, status). No further guidance is necessary given the tool's simplicity and the presence of an output schema.
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 has 0% description coverage for the parameter 'station', but the description adds important semantic detail by specifying that the ID is numeric and giving an example (8454000). This compensates for the schema's lack of field description, although the example is repeated from the schema. The description effectively clarifies the expected input format.
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 clearly states the function with a specific verb ('Fetch'), a specific resource ('full metadata for a single NOAA station'), and the input format ('numeric station ID'). It also lists the metadata content (location, available products, operational status), distinguishing it from the sibling 'stations' tool which likely lists multiple stations.
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?
The description provides clear context for when to use the tool: when you need full metadata for one specific station by numeric ID. It does not explicitly name alternative tools or state when not to use it, but the 'single station' qualifier implicitly separates it from listing tools like 'stations'. This meets the bar for 'clear context, no exclusions'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.