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RyanCardin15

LocalTides MCP Server

get_high_tide_flooding_projections

Generate high tide flooding projections for specific decades based on sea level rise scenarios, flood thresholds, and station data. Output available in JSON, XML, or CSV formats.

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

  • Core handler function implementing the tool logic by calling the NOAA DPAPI /htf/projections endpoint with processed parameters.
    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
      });
    }
  • Zod schema for input validation of the get_high_tide_flooding_projections tool parameters.
    export const HighTideFloodingProjectionsSchema = z.object({
      station: StationSchema,
      format: FormatSchema,
      scenario: ScenarioSchema,
      datum: DpapiDatumSchema,
      threshold: ThresholdSchema,
      decade: DecadeSchema
    }).describe('Get high tide flooding decadal projections');
  • MCP tool registration including name, description, schema reference, and thin execute wrapper delegating to the service handler.
    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');
        }
      }
    });
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 states the tool 'gets' projections, implying a read-only operation, but does not clarify authentication needs, rate limits, data freshness, or error handling. For a tool with no annotations and complex parameters, this lack of behavioral context is a significant gap.

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 redundancy. It is front-loaded with essential information and contains no unnecessary words, making it highly concise and well-structured for quick comprehension.

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 the tool's complexity (6 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details on output format, error conditions, or usage context. Without annotations or output schema, more behavioral and contextual information would improve completeness, but it meets a basic threshold.

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 all parameters well-documented in the schema itself (e.g., 'datum' as 'Datum reference for DPAPI', 'decade' as 'Decade for projections'). The description adds no additional parameter semantics beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting.

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 ('for sea level rise scenarios'), which is specific and actionable. However, it does not explicitly differentiate from sibling tools like 'get_high_tide_flooding_annual' or 'get_high_tide_flooding_monthly', which limits its score to 4.

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, prerequisites, or specific contexts for application. Without any usage instructions, the agent must infer from the tool name and schema alone, which is insufficient for optimal tool selection.

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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