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RyanCardin15

noaa-tidesandcurrents-mcp

get_high_tide_flooding_likelihoods

Analyze daily high tide flooding probabilities by station, flood threshold, and date. Choose output format (json, xml, csv) and datum reference to assess flood risks.

Instructions

Get high tide flooding daily likelihoods for a station

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoSpecific date (YYYYMMDD format)
datumNoDatum reference for DPAPI
formatNoOutput format (json, xml, csv)
stationYesStation ID
thresholdNoFlood threshold level

Implementation Reference

  • The tool's execute handler: thin wrapper that invokes the service method and returns JSON stringified result, with error handling.
    execute: async (params) => {
      try {
        const result = await dpapiService.getHighTideFloodingLikelihoods(params);
        return JSON.stringify(result);
      } catch (error) {
        if (error instanceof Error) {
          throw new Error(`Failed to get high tide flooding likelihoods: ${error.message}`);
        }
        throw new Error('Failed to get high tide flooding likelihoods');
      }
    }
  • Zod schema defining input parameters for the tool: station, format, datum, threshold, optional date.
    export const HighTideFloodingLikelihoodsSchema = z.object({
      station: StationSchema,
      format: FormatSchema,
      datum: DpapiDatumSchema,
      threshold: ThresholdSchema,
      date: z.string().optional().describe('Specific date (YYYYMMDD format)')
    }).describe('Get high tide flooding daily likelihoods');
  • Registration of the tool with the MCP server using server.addTool, including name, description, schema, and handler.
    server.addTool({
      name: 'get_high_tide_flooding_likelihoods',
      description: 'Get high tide flooding daily likelihoods for a station',
      parameters: HighTideFloodingLikelihoodsSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getHighTideFloodingLikelihoods(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get high tide flooding likelihoods: ${error.message}`);
          }
          throw new Error('Failed to get high tide flooding likelihoods');
        }
      }
    });
  • Supporting service method in DpapiService that performs the actual API fetch to the /htf/likelihoods endpoint.
    async getHighTideFloodingLikelihoods(params: Record<string, any>): Promise<any> {
      const { station, format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/htf/likelihoods', {
        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 the full burden of behavioral disclosure. While 'Get' implies a read-only operation, the description doesn't address important aspects like authentication requirements, rate limits, error conditions, or what the output looks like (e.g., data format, structure). For a tool with 5 parameters and no annotations, this is insufficient.

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 communicates the core purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information, making it easy for an agent to parse quickly.

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 (5 parameters, no output schema, no annotations), the description is minimally adequate but has clear gaps. It states what the tool does but doesn't provide behavioral context or usage guidance. With no output schema, the agent doesn't know what to expect in return, which is a significant limitation for effective tool 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 all parameters well-documented in the input schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema (e.g., it doesn't explain relationships between parameters or provide usage examples). Baseline 3 is appropriate when 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 verb 'Get' and the resource 'high tide flooding daily likelihoods for a station', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_high_tide_flooding_daily' or 'get_high_tide_flooding_annual', which appear to be related alternatives.

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_high_tide_flooding_daily' and 'get_high_tide_flooding_annual' that seem related, there's no indication of what distinguishes this tool's functionality from those, leaving the agent without context for 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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