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MCP Avantage

by MissionSquad

economicIndicators_durableGoodsOrders

Access US durable goods orders data to analyze manufacturing trends and economic health, supporting informed financial decisions.

Instructions

Retrieves US durable goods orders data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datatypeNoData format for the response.json

Implementation Reference

  • src/index.ts:660-674 (registration)
    Registers the 'economicIndicators_durableGoodsOrders' tool with the MCP server, specifying name, description, input schema, and execution handler that delegates to executeAvantageTool and the Avantage library's economicIndicators.durableGoodsOrders method.
    server.addTool({
      name: "economicIndicators_durableGoodsOrders",
      description: "Retrieves US durable goods orders data.",
      parameters: schemas.EconomicIndicatorsDataTypeParamSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "economicIndicators_durableGoodsOrders",
          args,
          context,
          (av, params) => av.economicIndicators.durableGoodsOrders(params)
        ),
    });
  • Defines the Zod input validation schema for the tool, allowing an optional 'datatype' parameter (json or csv).
    export const EconomicIndicatorsDataTypeParamSchema = z.object({
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Common parameter schema accepting only datatype.')
  • Shared handler function that implements the core tool execution logic: resolves API key, manages AVantage client instance via resourceManager, invokes the specific Avantage method, handles errors, and returns JSON data.
    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}`
        );
      }
    }
  • Specific callback passed to executeAvantageTool that invokes the Avantage library's economicIndicators.durableGoodsOrders method with validated parameters.
          (av, params) => av.economicIndicators.durableGoodsOrders(params)
        ),
    });
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 states 'retrieves,' implying a read-only operation, but doesn't specify data sources, update frequency, rate limits, authentication needs, or error handling. For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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: 'Retrieves US durable goods orders data.' It's front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple retrieval tool.

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 low complexity (1 optional parameter, no output schema, no annotations), the description is minimally adequate. It states what data is retrieved but lacks context on data recency, granularity, or comparison to siblings. Without annotations or output schema, more behavioral details would improve completeness, but it meets the baseline for a simple tool.

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, with one parameter ('datatype') fully documented in the schema. The description doesn't add any parameter-specific information beyond what the schema provides, such as default behavior or data format implications. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description.

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 tool's purpose: 'Retrieves US durable goods orders data.' It specifies the verb ('retrieves'), resource ('US durable goods orders data'), and scope ('US'). However, it doesn't explicitly differentiate from sibling tools like 'economicIndicators_cpi' or 'economicIndicators_retailSales' beyond the resource name, which is why it doesn't achieve a perfect score.

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. It doesn't mention any prerequisites, constraints, or sibling tools for comparison. The agent must infer usage based on the tool name alone, which is insufficient for optimal 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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