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RyanCardin15

LocalTides MCP Server

get_high_tide_flooding_annual

Retrieve annual high tide flooding counts for a specific station based on station ID, flood threshold, date range, and preferred output format (JSON, XML, CSV).

Instructions

Get high tide flooding annual 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)
stationYesStation ID
thresholdNoFlood threshold level
year_rangeNoYear range (YYYY-YYYY format)

Implementation Reference

  • The execute handler for the get_high_tide_flooding_annual tool, which delegates to the dpapiService.getHighTideFloodingAnnual method and returns the JSON-stringified result, with error handling.
    execute: async (params) => {
      try {
        const result = await dpapiService.getHighTideFloodingAnnual(params);
        return JSON.stringify(result);
      } catch (error) {
        if (error instanceof Error) {
          throw new Error(`Failed to get high tide flooding annual data: ${error.message}`);
        }
        throw new Error('Failed to get high tide flooding annual data');
      }
  • Zod schema defining the input parameters and validation for the get_high_tide_flooding_annual tool.
    export const HighTideFloodingAnnualSchema = z.object({
      station: StationSchema,
      format: FormatSchema,
      datum: DpapiDatumSchema,
      threshold: ThresholdSchema,
      begin_date: z.string().optional().describe('Start date (YYYYMMDD format)'),
      end_date: z.string().optional().describe('End date (YYYYMMDD format)'),
      year_range: YearRangeSchema
    }).describe('Get high tide flooding annual count data');
  • Registers the get_high_tide_flooding_annual tool with the FastMCP server, including name, description, schema, and handler.
    server.addTool({
      name: 'get_high_tide_flooding_annual',
      description: 'Get high tide flooding annual count data for a station',
      parameters: HighTideFloodingAnnualSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getHighTideFloodingAnnual(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get high tide flooding annual data: ${error.message}`);
          }
          throw new Error('Failed to get high tide flooding annual data');
        }
      }
    });
  • DpapiService method that makes the API request to NOAA's /htf/annual endpoint for high tide flooding annual data.
    async getHighTideFloodingAnnual(params: Record<string, any>): Promise<any> {
      const { station, format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/htf/annual', {
        station,
        format,
        ...rest
      });
    }
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states it 'gets' data (implying read-only), but doesn't disclose behavioral traits like rate limits, authentication needs, data freshness, or what 'annual count data' entails (e.g., aggregated statistics). For a data retrieval tool with no annotations, this leaves significant gaps in understanding how it behaves.

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 with zero waste. It's front-loaded with the core purpose, making it easy to parse quickly. Every word earns its place without redundancy.

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?

Given 7 parameters, no annotations, and no output schema, the description is minimally adequate but incomplete. It specifies the data type ('annual count data') and target ('station'), but doesn't explain return values, error conditions, or how it differs from sibling tools. For a tool in a crowded namespace, more context would help the agent use it correctly.

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?

Schema description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no parameter semantics beyond implying 'station' is required (matching schema). Baseline 3 is appropriate when the schema does the heavy lifting, though the description could have clarified parameter interactions (e.g., date vs. year_range).

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 verb ('Get') and resource ('high tide flooding annual count data for a station'), making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_high_tide_flooding_daily' or 'get_high_tide_flooding_monthly', which would require specifying the temporal aggregation level (annual vs. daily/monthly).

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?

No guidance is provided on when to use this tool versus alternatives. With multiple high-tide flooding tools (daily, monthly, annual, likelihoods, projections), the description lacks context about temporal scope, data granularity, or use cases, leaving the agent to infer from tool names alone.

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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