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RyanCardin15

noaa-tidesandcurrents-mcp

get_sea_level_trends

Retrieve sea level trends and error margins for specific stations using station ID and output in JSON, XML, or CSV formats. Ideal for analyzing NOAA Tides and Currents data.

Instructions

Get sea level trends and error margins for a station

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
affilNoStation affiliation (Global or US)
formatNoOutput format (json, xml, csv)
stationYesStation ID

Implementation Reference

  • Core handler function that fetches sea level trends data from the NOAA DPAPI by building parameters and calling the generic fetchDpapi method.
    async getSeaLevelTrends(params: Record<string, any>): Promise<any> {
      const { station, affil = 'Global', format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/sltrends', {
        station,
        affil,
        format,
        ...rest
      });
    }
  • Zod schema for input validation of the get_sea_level_trends tool parameters: station, affiliation (affil), and format.
    export const SeaLevelTrendsSchema = z.object({
      station: StationSchema,
      affil: AffiliationSchema,
      format: FormatSchema
    }).describe('Get sea level trends for a station');
  • Registers the 'get_sea_level_trends' tool with the FastMCP server, specifying name, description, input schema, and an execute handler that calls the DpapiService and returns JSON stringified result.
    server.addTool({
      name: 'get_sea_level_trends',
      description: 'Get sea level trends and error margins for a station',
      parameters: SeaLevelTrendsSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getSeaLevelTrends(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get sea level trends: ${error.message}`);
          }
          throw new Error('Failed to get sea level trends');
        }
      }
    });
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions retrieving 'trends and error margins,' which implies a read-only operation, but doesn't specify data freshness, rate limits, authentication requirements, or what the output format looks like (beyond the schema's format parameter). For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given the lack of annotations and output schema, the description is insufficient for a tool with 3 parameters and multiple siblings. It doesn't explain the return values (e.g., what 'trends' include, units, time periods), behavioral constraints, or differentiation from other station data tools, leaving the agent with incomplete context for effective use.

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?

The schema description coverage is 100%, with clear descriptions for all parameters (station ID, affiliation, format). The description adds no additional parameter semantics beyond what the schema provides, such as explaining what 'trends' entail or how error margins are calculated. With high schema coverage, the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the action ('Get') and resource ('sea level trends and error margins for a station'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from its many siblings that also retrieve various station-related data (e.g., get_current_predictions, get_water_levels), leaving room for ambiguity about when to choose this specific trend-focused tool.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. With multiple sibling tools like get_station_details, get_water_levels, and get_high_tide_flooding_annual that might overlap in station-based data retrieval, there's no indication of what makes this tool unique or when it's the appropriate choice.

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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