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MissionSquad

MCP Avantage

by MissionSquad

economicIndicators_inflation

Retrieve US inflation rate data to analyze economic trends and inform financial decisions using Alpha Vantage API.

Instructions

Retrieves US inflation rate data.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datatypeNoData format for the response.json

Implementation Reference

  • src/index.ts:628-642 (registration)
    Registers the 'economicIndicators_inflation' MCP tool with name, description, input schema, and execute handler that delegates to the generic executeAvantageTool function calling the AVantage library's economicIndicators.inflation method.
    server.addTool({
      name: "economicIndicators_inflation",
      description: "Retrieves US inflation rate data.",
      parameters: schemas.EconomicIndicatorsDataTypeParamSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "economicIndicators_inflation",
          args,
          context,
          (av, params) => av.economicIndicators.inflation(params)
        ),
    });
  • Defines the Zod input parameters schema for the tool, allowing an optional 'datatype' field (json or csv).
    export const EconomicIndicatorsDataTypeParamSchema = z.object({
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Common parameter schema accepting only datatype.')
  • Generic helper function that implements the core execution logic for all AVantage-based tools: resolves API key, manages AVantage client instance, invokes the provided library method (av.economicIndicators.inflation for this tool), handles errors, and returns JSON-stringified 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 of behavioral disclosure. While 'Retrieves' implies a read operation, it doesn't mention important aspects like data sources, update frequency, rate limits, authentication requirements, or error handling. This leaves significant gaps for an AI agent to understand how to use it effectively.

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 gets straight to the point with no wasted words. It's appropriately sized for a simple data retrieval tool and is perfectly front-loaded with the core purpose.

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 the inflation data includes (e.g., time periods, metrics like CPI), how it's formatted, or what the response looks like. Given the complexity of economic data and lack of structured information, more context is needed for effective use.

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 (including enum values and default). The description adds no additional parameter information beyond what the schema provides, which is acceptable given the high schema coverage, resulting in the baseline score.

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 inflation rate data'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'economicIndicators_cpi' (which might retrieve similar data), so it doesn't reach the highest 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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools in the economicIndicators category (e.g., cpi, unemploymentRate, realGDP), the description offers no context about what makes this tool distinct or when it should be preferred over others.

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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