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noaa-tidesandcurrents-mcp

get_high_tide_flooding_projections

Project future high tide flooding events by analyzing sea level rise scenarios, station data, and flood thresholds using NOAA Tides and Currents API. Choose output formats like JSON, XML, or CSV for detailed insights.

Instructions

Get high tide flooding decadal projections for sea level rise scenarios

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datumNoDatum reference for DPAPI
decadeNoDecade for projections (e.g., "2050")
formatNoOutput format (json, xml, csv)
scenarioNoSea level rise scenario
stationYesStation ID
thresholdNoFlood threshold level

Implementation Reference

  • Registers the get_high_tide_flooding_projections MCP tool with FastMCP server.addTool, providing name, description, input parameters schema, and an execute handler that calls the DpapiService and returns JSON stringified result.
    server.addTool({
      name: 'get_high_tide_flooding_projections',
      description: 'Get high tide flooding decadal projections for sea level rise scenarios',
      parameters: HighTideFloodingProjectionsSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getHighTideFloodingProjections(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get high tide flooding projections: ${error.message}`);
          }
          throw new Error('Failed to get high tide flooding projections');
        }
      }
    });
  • Zod schema defining the input parameters for the get_high_tide_flooding_projections tool, including station, format, scenario, datum, threshold, and decade.
    export const HighTideFloodingProjectionsSchema = z.object({
      station: StationSchema,
      format: FormatSchema,
      scenario: ScenarioSchema,
      datum: DpapiDatumSchema,
      threshold: ThresholdSchema,
      decade: DecadeSchema
    }).describe('Get high tide flooding decadal projections');
  • DpapiService helper method implementing the core logic: extracts parameters and calls NOAA DPAPI endpoint '/htf/projections' via fetchDpapi to retrieve high tide flooding projections data.
    async getHighTideFloodingProjections(params: Record<string, any>): Promise<any> {
      const { station, scenario = 'all', format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/htf/projections', {
        station,
        scenario,
        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 the full burden of behavioral disclosure. The description only states what the tool does without revealing any behavioral traits such as data format, rate limits, authentication needs, or potential side effects. For a tool with multiple parameters and no annotations, 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, clear sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded and to the point, making it easy to parse and understand quickly. Every part of the description earns its place by specifying key elements like 'decadal projections' and 'sea level rise scenarios.'

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 complexity of the tool (6 parameters, no annotations, no output schema), the description is insufficient. It lacks details on behavioral aspects, usage context, and output expectations. Without annotations or an output schema, the description should provide more context about what the tool returns and how to interpret results, but it does not, leaving significant gaps for an AI agent.

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 input schema has 100% description coverage, with detailed descriptions and enums for parameters like 'datum,' 'decade,' 'format,' 'scenario,' 'station,' and 'threshold.' The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline score of 3, as the schema adequately documents the parameters.

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 decadal projections for sea level rise scenarios.' It specifies the verb ('Get'), resource ('high tide flooding decadal projections'), and context ('sea level rise scenarios'), making it easy to understand. However, it does not explicitly differentiate from sibling tools like 'get_high_tide_flooding_annual' or 'get_high_tide_flooding_monthly,' which reduces clarity in distinguishing between similar tools.

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. It does not mention sibling tools, specify scenarios where this tool is preferred, or outline any prerequisites or exclusions. This lack of context makes it challenging for an AI agent to select this tool appropriately among the many related flooding and prediction tools available.

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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