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RyanCardin15

LocalTides MCP Server

get_sea_level_trends

Retrieve sea level trends and error margins for a specific station in JSON, XML, or CSV format using the LocalTides MCP Server tool, designed for analyzing water level data efficiently.

Instructions

Get sea level trends and error margins for a station

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
affilNoStation affiliation (Global or US)
formatNoOutput format (json, xml, csv)
stationYesStation ID

Implementation Reference

  • The execute handler function for the 'get_sea_level_trends' tool. It calls the DpapiService.getSeaLevelTrends method with the input params, stringifies the result, and includes error handling.
    execute: async (params) => {
      try {
        const result = await dpapiService.getSeaLevelTrends(params);
        return JSON.stringify(result);
      } catch (error) {
        if (error instanceof Error) {
          throw new Error(`Failed to get sea level trends: ${error.message}`);
        }
        throw new Error('Failed to get sea level trends');
      }
    }
  • Zod schema defining the input parameters for the get_sea_level_trends tool: station (required), affil (optional), format (optional).
    export const SeaLevelTrendsSchema = z.object({
      station: StationSchema,
      affil: AffiliationSchema,
      format: FormatSchema
    }).describe('Get sea level trends for a station');
  • Registration of the 'get_sea_level_trends' tool on the FastMCP server within the registerDerivedProductTools function.
    server.addTool({
      name: 'get_sea_level_trends',
      description: 'Get sea level trends and error margins for a station',
      parameters: SeaLevelTrendsSchema,
      execute: async (params) => {
        try {
          const result = await dpapiService.getSeaLevelTrends(params);
          return JSON.stringify(result);
        } catch (error) {
          if (error instanceof Error) {
            throw new Error(`Failed to get sea level trends: ${error.message}`);
          }
          throw new Error('Failed to get sea level trends');
        }
      }
    });
  • Helper service method in DpapiService that performs the actual API request to NOAA's DPAPI /sltrends endpoint for sea level trends data.
    async getSeaLevelTrends(params: Record<string, any>): Promise<any> {
      const { station, affil = 'Global', format = 'json', ...rest } = params;
      
      return this.fetchDpapi('/sltrends', {
        station,
        affil,
        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. It mentions 'trends and error margins' but doesn't specify data format, time ranges, or any limitations (e.g., rate limits, authentication needs). For a read operation with no annotation coverage, this leaves significant gaps in understanding how the tool 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 that front-loads the core purpose without unnecessary words. It's appropriately sized for a simple tool, with zero waste or 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 no annotations and no output schema, the description is minimal but covers the basic purpose. However, for a tool that likely returns complex data (trends and error margins), it lacks details on output structure or behavioral context. It's adequate as a starting point but incomplete for full agent understanding.

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 input schema already documents all parameters (station, affil, format) with descriptions and enums. The description adds no additional meaning beyond implying station-based retrieval, which is already clear from the schema. Baseline 3 is appropriate as 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 action ('Get') and the resource ('sea level trends and error margins for a station'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from siblings like 'get_station_details' or 'get_water_levels', which might also provide station-related data, so it misses full sibling distinction.

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 many sibling tools available (e.g., 'get_station_details', 'get_water_levels'), there's no indication of specific use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name 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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