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MissionSquad

MCP Avantage

by MissionSquad

economicIndicators_retailSales

Retrieve US retail sales data to analyze consumer spending trends and economic health using Alpha Vantage financial data.

Instructions

Retrieves US retail sales data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datatypeNoData format for the response.json

Implementation Reference

  • src/index.ts:644-658 (registration)
    Registers the 'economicIndicators_retailSales' MCP tool, defining its name, description, input schema, and thin execute handler that delegates to executeAvantageTool which calls the Avantage library's retailSales method.
    server.addTool({
      name: "economicIndicators_retailSales",
      description: "Retrieves US retail sales data.",
      parameters: schemas.EconomicIndicatorsDataTypeParamSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "economicIndicators_retailSales",
          args,
          context,
          (av, params) => av.economicIndicators.retailSales(params)
        ),
    });
  • Zod schema defining optional 'datatype' parameter (json or csv) for economic indicators tools including retailSales.
    export const EconomicIndicatorsDataTypeParamSchema = z.object({
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Common parameter schema accepting only datatype.')
  • Shared helper function that handles authentication, resource management for AVantage client instances, executes the provided library method (av.economicIndicators.retailSales), processes results, and returns JSON string. Used by all tools.
    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}`
        );
      }
    }
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 only states the retrieval action without disclosing behavioral traits like data freshness, source, rate limits, authentication needs, or error handling. For a data-fetching tool, this lack of operational 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, efficient sentence that directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the retrieved data includes (e.g., time series, metrics), how it's structured, or any prerequisites. For a tool in a complex server with many siblings, more context is needed to use it effectively.

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 the single parameter 'datatype' fully documented in the schema. The description adds no parameter-specific information beyond what the schema provides, so it meets the baseline of 3 without compensating or adding extra value.

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 ('Retrieves') and resource ('US retail sales data'), providing a specific purpose. However, it doesn't differentiate from sibling tools like 'economicIndicators_cpi' or 'economicIndicators_durableGoodsOrders' that also retrieve economic data, leaving room for confusion about when to choose this specific indicator.

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 offers no guidance on when to use this tool versus alternatives. With many sibling tools for economic indicators (e.g., CPI, GDP, unemployment), there's no indication of what makes retail sales data unique or when it's preferred, leaving the agent to guess based on context 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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