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RyanCardin15

LocalTides MCP Server

get_meteorological_data

Retrieve meteorological data for specific stations, products, and date ranges using customizable parameters like units, time zones, and output formats with the LocalTides MCP Server.

Instructions

Get meteorological data

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
begin_dateNoStart date (YYYYMMDD or MM/DD/YYYY)
dateNoDate to retrieve data for ("today", "latest", "recent", or specific date)
end_dateNoEnd date (YYYYMMDD or MM/DD/YYYY)
formatNoOutput format (json, xml, csv)
productYesProduct (air_temperature, wind, etc.)
rangeNoNumber of hours to retrieve data for
stationYesStation ID
time_zoneNoTime zone (gmt, lst, lst_ldt)
unitsNoUnits to use ("english" or "metric")

Implementation Reference

  • The core handler function that implements the logic for fetching meteorological data from the NOAA API by delegating to fetchDataApi with the product parameter.
    async getMeteorologicalData(params: Record<string, any>): Promise<any> {
      const { product, ...rest } = params;
      return this.fetchDataApi({
        ...rest,
        product
      });
    }
  • Zod schema defining the input parameters and validation for the get_meteorological_data tool.
    export const GetMeteorologicalDataSchema = z.object({
      station: StationSchema,
      product: z.string().min(1).describe('Product (air_temperature, wind, etc.)'),
      date: DateSchema,
      begin_date: BeginDateSchema,
      end_date: EndDateSchema,
      range: RangeSchema,
      units: UnitsSchema,
      time_zone: TimeZoneSchema,
      format: FormatSchema,
    }).refine(
      data => (data.date || (data.begin_date && data.end_date) || (data.begin_date && data.range) || (data.end_date && data.range) || data.range),
      { message: "You must provide either 'date', 'begin_date' and 'end_date', 'begin_date' and 'range', 'end_date' and 'range', or just 'range'" }
    );
  • Registration of the get_meteorological_data tool in the custom McpServer class, using the schema and delegating to NoaaService.
    const getMeteorologicalData: MCPTool = {
      name: "get_meteorological_data",
      description: "Get meteorological data",
      inputSchema: GetMeteorologicalDataSchema,
      handler: async (params) => {
        return this.noaaService.getMeteorologicalData(params);
      }
    };
  • Registration of the get_meteorological_data tool for the FastMCP server, with inline schema and delegation to NoaaService.
    server.addTool({
      name: 'get_meteorological_data',
      description: 'Get meteorological data',
      parameters: z.object({
        station: StationSchema,
        product: z.string().min(1).describe('Product (air_temperature, wind, etc.)'),
        date: DateSchema,
        begin_date: BeginDateSchema,
        end_date: EndDateSchema,
        range: RangeSchema,
        units: UnitsSchema,
        time_zone: TimeZoneSchema,
        format: FormatSchema,
      }).refine(refineDateParams, { message: dateRefinementMessage }),
      execute: async (params) => {
        try {
          const result = await noaaService.getMeteorologicalData(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get meteorological data: ${error.message}`);
          }
          throw new Error('Failed to get meteorological data');
        }
      }
    });
Behavior1/5

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

No annotations are provided, so the description carries full burden for behavioral disclosure. 'Get meteorological data' reveals nothing about authentication requirements, rate limits, data freshness, error conditions, or response format. For a tool with 9 parameters and no output schema, this leaves the agent completely in the dark about how the tool behaves and what to expect from its execution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While technically concise with only three words, this is under-specification rather than effective conciseness. The description fails to front-load essential information about what makes this tool distinct. Every word should earn its place, but 'Get meteorological data' provides almost no value beyond the tool name itself, making it inefficient despite its brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, 2 required), lack of annotations, and absence of an output schema, the description is completely inadequate. It doesn't explain what meteorological data is returned, in what structure, or how it relates to the many sibling tools. For a data retrieval tool with significant parameter complexity, this minimal description leaves critical gaps in understanding the tool's purpose and behavior.

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%, with all 9 parameters well-documented in the input schema. The description adds no parameter information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description. The description doesn't compensate for any gaps because there are none in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get meteorological data' is a tautology that restates the tool name without adding specificity. It doesn't specify what meteorological data is retrieved (e.g., temperature, wind, precipitation) or how it differs from sibling tools like 'get_current_predictions' or 'get_water_levels'. The description fails to distinguish this tool's unique scope within the meteorological domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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 20+ sibling tools on the server, there's no indication of whether this tool is for historical data, current conditions, forecasts, or specialized meteorological parameters. The agent receives no help in selecting this tool over other meteorological or environmental data tools.

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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