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by MissionSquad

economicIndicators_cpi

Retrieve US Consumer Price Index (CPI) data to analyze inflation trends. Access monthly or semiannual data in JSON or CSV format for economic analysis.

Instructions

Retrieves US Consumer Price Index (CPI) data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intervalNoTime interval.monthly
datatypeNoData format for the response.json

Implementation Reference

  • src/index.ts:615-626 (registration)
    Registers the 'economicIndicators_cpi' MCP tool, defining its name, description, input schema, and execution handler that delegates to the Alpha Vantage library via executeAvantageTool.
    server.addTool({
      name: "economicIndicators_cpi",
      description: "Retrieves US Consumer Price Index (CPI) data.",
      parameters: schemas.EconomicIndicatorsCPIParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("economicIndicators_cpi", args, context, (av, params) =>
          av.economicIndicators.cpi(params)
        ),
    });
  • Zod schema defining the input parameters for the economicIndicators_cpi tool (interval: monthly|semiannual, datatype: json|csv).
    export const EconomicIndicatorsCPIParamsSchema = z.object({
      interval: z.enum(['monthly', 'semiannual']).default('monthly').optional().describe('Time interval.'),
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Parameters for fetching CPI data.')
  • Generic helper function used by all tools, including economicIndicators_cpi, to manage AVantage client lifecycle, execute the specific library method (e.g., av.economicIndicators.cpi), and return 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}`
        );
      }
    }
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. 'Retrieves' implies a read-only operation, but it doesn't disclose any behavioral traits like rate limits, authentication requirements, data freshness, or potential errors. For a data-fetching tool with no annotation coverage, this 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 with zero waste. It's front-loaded with the core purpose and appropriately sized for a simple data retrieval tool.

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 complexity (a data retrieval tool with no annotations and no output schema), the description is incomplete. It doesn't explain what the returned CPI data includes (e.g., time series, values, metadata), how it's structured, or any usage constraints. For a tool fetching economic data, more context is needed.

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 schema fully documents both parameters (interval and datatype) with enums and defaults. The description adds no parameter information beyond what's in the schema, meeting the baseline of 3 when 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 verb ('Retrieves') and resource ('US Consumer Price Index (CPI) data'), making the purpose immediately understandable. It doesn't differentiate from sibling tools like 'economicIndicators_inflation' or 'economicIndicators_realGDP', which is why it doesn't reach a 5.

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 in the economicIndicators category (e.g., inflation, realGDP, unemploymentRate), there's no indication of when CPI data is specifically needed or how it differs from other economic indicators.

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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