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RyanCardin15

noaa-tidesandcurrents-mcp

get_high_tide_flooding_seasonal

Retrieve seasonal high tide flooding counts for a specific station, filtered by threshold, datum, and time range, in JSON, XML, or CSV format, to analyze coastal flooding trends.

Instructions

Get high tide flooding seasonal count data for a station

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
begin_dateNoStart date (YYYYMMDD format)
datumNoDatum reference for DPAPI
end_dateNoEnd date (YYYYMMDD format)
formatNoOutput format (json, xml, csv)
season_monthsNoSeason months (DJF-Winter, MAM-Spring, JJA-Summer, SON-Fall)
stationYesStation ID
thresholdNoFlood threshold level
yearNoYear for analysis (YYYY format)

Implementation Reference

  • Core handler function that fetches high tide flooding seasonal data from the DPAPI '/htf/seasonal' endpoint using the service's fetch method.
    async getHighTideFloodingSeasonal(params: Record<string, any>): Promise<any> {
      const { station, format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/htf/seasonal', {
        station,
        format,
        ...rest
      });
    }
  • Zod schema defining input parameters for the get_high_tide_flooding_seasonal tool.
    export const HighTideFloodingSeasonalSchema = z.object({
      station: StationSchema,
      format: FormatSchema,
      datum: DpapiDatumSchema,
      threshold: ThresholdSchema,
      season_months: SeasonSchema,
      begin_date: z.string().optional().describe('Start date (YYYYMMDD format)'),
      end_date: z.string().optional().describe('End date (YYYYMMDD format)'),
      year: YearSchema
    }).describe('Get high tide flooding seasonal count data');
  • Registers the 'get_high_tide_flooding_seasonal' tool with MCP server, including schema and thin wrapper execute handler that delegates to the service.
    server.addTool({
      name: 'get_high_tide_flooding_seasonal',
      description: 'Get high tide flooding seasonal count data for a station',
      parameters: HighTideFloodingSeasonalSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getHighTideFloodingSeasonal(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get high tide flooding seasonal data: ${error.message}`);
          }
          throw new Error('Failed to get high tide flooding seasonal data');
        }
      }
    });
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 only states what the tool does ('Get... data') without detailing aspects like whether it's a read-only operation, potential rate limits, authentication needs, error handling, or data freshness. For a tool with 8 parameters and no annotation coverage, this is a significant gap in transparency.

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: 'Get high tide flooding seasonal count data for a station.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for the tool's complexity. Every part of the sentence earns its place by specifying the action, data type, and target.

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 tool's complexity (8 parameters, no output schema, no annotations), the description is incomplete. It lacks information on return values (e.g., data structure, units), usage context relative to siblings, and behavioral traits like error conditions or performance. For a data retrieval tool with multiple parameters and no structured output documentation, the description should provide more guidance to be fully helpful.

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 detailed descriptions for all 8 parameters, including enums and formats. The description adds no additional parameter semantics beyond the schema, such as explaining interactions between parameters (e.g., how 'season_months' relates to 'begin_date' and 'end_date'). Given the high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 tool's purpose: 'Get high tide flooding seasonal count data for a station.' It specifies the verb ('Get'), resource ('high tide flooding seasonal count data'), and scope ('for a station'), which is specific and actionable. However, it doesn't explicitly differentiate from its siblings like 'get_high_tide_flooding_annual' or 'get_high_tide_flooding_monthly' beyond the 'seasonal' qualifier, which is why it doesn't reach a 5.

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 for high tide flooding data (e.g., annual, daily, monthly, seasonal), it fails to specify scenarios where seasonal data is preferred, prerequisites, or exclusions. This leaves the agent without context for tool selection among similar options.

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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