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MissionSquad

MCP Avantage

by MissionSquad

economicIndicators_nonfarmPayroll

Retrieve US nonfarm payroll data to analyze employment trends and economic health for informed financial decisions.

Instructions

Retrieves US nonfarm payroll data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datatypeNoData format for the response.json

Implementation Reference

  • src/index.ts:692-706 (registration)
    Registers the 'economicIndicators_nonfarmPayroll' tool with the MCP server, including name, description, input schema reference, and execute handler that calls the shared executeAvantageTool helper with the specific Alpha Vantage library method.
    server.addTool({
      name: "economicIndicators_nonfarmPayroll",
      description: "Retrieves US nonfarm payroll data.",
      parameters: schemas.EconomicIndicatorsDataTypeParamSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "economicIndicators_nonfarmPayroll",
          args,
          context,
          (av, params) => av.economicIndicators.nonfarmPayroll(params)
        ),
    });
  • Zod schema defining the input parameters for the economicIndicators_nonfarmPayroll tool, which only accepts an optional 'datatype' field defaulting to 'json'.
    export const EconomicIndicatorsDataTypeParamSchema = z.object({
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Common parameter schema accepting only datatype.')
  • Shared handler logic executed by the tool's execute function. Manages Alpha Vantage API key, creates/reuses AVantage client instance, invokes the specific library method av.economicIndicators.nonfarmPayroll(params), processes response, and returns JSON data or throws errors.
    async function executeAvantageTool<TArgs, TResult>(
      toolName: string,
      args: TArgs,
      context: Context<Record<string, unknown> | undefined>, // Use the imported Context type directly
      avantageMethod: (
        av: AVantage,
        args: TArgs
      ) => Promise<{ error?: boolean; reason?: string; data?: TResult }>
    ): Promise<string> {
      logger.info(`Executing '${toolName}' tool for request ID: ${context}`);
      logger.debug(`Args for ${toolName}: ${JSON.stringify(args)}`);
    
      // --- Authentication & Resource Management ---
      // Access extraArgs safely - it might be null or undefined
      const extraArgsApiKey = context.extraArgs?.apiKey as string | undefined;
      const apiKey = extraArgsApiKey || config.apiKey;
    
      if (!apiKey) {
        logger.error(`'${toolName}' failed: Alpha Vantage API key missing.`);
        throw new UserError(apiKeyErrorMessage);
      }
      logger.debug(
        `Using AV API key (source: ${extraArgsApiKey ? "extraArgs" : "environment"}) for ${toolName}`
      );
    
      try {
        // Get or create AVantage instance managed by ResourceManager
        const av = await resourceManager.getResource<AVantage>(
          apiKey, // Cache key is the resolved API key
          "avantage_client", // Type identifier for logging
          async (key) => {
            // Factory Function
            logger.info(
              `Creating new AVantage instance for key ending ...${key.slice(-4)}`
            );
            // AVantage library reads AV_PREMIUM from process.env internally
            return new AVantage(key);
          },
          async (avInstance) => {
            // Cleanup Function (no-op needed for AVantage)
            logger.debug(`Destroying AVantage instance (no-op)`);
          }
        );
    
        // --- Library Call ---
        const result = await avantageMethod(av, args);
    
        // --- Response Handling ---
        if (result.error) {
          logger.warn(
            `'${toolName}' failed. Reason from avantage: ${result.reason}`
          );
          throw new UserError(result.reason || `Tool '${toolName}' failed.`);
        }
    
        if (result.data === undefined || result.data === null) {
          logger.warn(`'${toolName}' completed successfully but returned no data.`);
          return "null"; // Return string "null" for empty data
        }
    
        logger.info(`'${toolName}' completed successfully.`);
        // Stringify the data part of the response
        return JSON.stringify(result.data);
      } catch (error: any) {
        logger.error(
          `Error during execution of '${toolName}': ${error.message}`,
          error
        );
        // If it's already a UserError, rethrow it
        if (error instanceof UserError) {
          throw error;
        }
        // Otherwise, wrap it in a UserError
        throw new UserError(
          `An unexpected error occurred while executing tool '${toolName}': ${error.message}`
        );
      }
    }
  • Although categorized separately, this schema is a supporting utility for input validation in the tool registration.
    export const EconomicIndicatorsDataTypeParamSchema = z.object({
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Common parameter schema accepting only datatype.')
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states 'Retrieves' which implies a read operation, but doesn't disclose any behavioral traits like data freshness, source, rate limits, authentication requirements, or what the response contains. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding how it 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 states exactly what the tool does with zero wasted words. It's appropriately sized for a simple data retrieval tool and front-loads the core purpose immediately.

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?

For a data retrieval tool with no annotations and no output schema, the description is insufficient. It doesn't explain what data is returned, in what format, with what granularity, or from what time period. While concise, it leaves too many unanswered questions about what the tool actually provides.

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, fully documenting the single 'datatype' parameter with enum values and default. The description adds no parameter information beyond what's in the schema, so it meets the baseline score of 3 where 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 ('Retrieves') and resource ('US nonfarm payroll data'), making the purpose immediately understandable. It distinguishes from many siblings by specifying the economic indicator type, though it doesn't explicitly differentiate from other economicIndicators_* tools like 'economicIndicators_unemploymentRate' or 'economicIndicators_cpi' beyond the name.

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 economic indicator tools available (e.g., economicIndicators_cpi, economicIndicators_unemploymentRate), there's no indication of what makes nonfarm payroll data distinct or when it's most appropriate to retrieve this specific dataset.

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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