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MCP Avantage

by MissionSquad

commodities_wheat

Retrieve wheat commodity prices with configurable time intervals and data formats to analyze market trends and inform agricultural or investment decisions.

Instructions

Retrieves wheat prices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intervalNoTime interval for the data.
datatypeNoResponse data format.

Implementation Reference

  • src/index.ts:261-272 (registration)
    Registers the 'commodities_wheat' tool with the MCP server, including name, description, input schema from schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema, and an execute handler that uses the generic executeAvantageTool to call the AVantage library's commodities.wheat method.
    server.addTool({
      name: "commodities_wheat",
      description: "Retrieves wheat prices.",
      parameters: schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_wheat", args, context, (av, params) =>
          av.commodities.wheat(params)
        ),
    });
  • Defines the Zod input schema for the commodities_wheat tool, accepting optional 'interval' (monthly, quarterly, annual) and 'datatype' (json, csv).
    export const CommoditiesMonthlyQuarterlyAnnualParamsSchema = z.object({
      interval: MonthlyQuarterlyAnnualSchema.optional().describe('Time interval for the data.'),
      datatype: DatatypeSchema.optional().describe('Response data format.'),
    }).describe('Parameters for monthly/quarterly/annual commodity data.')
  • Generic handler function used by commodities_wheat (and all tools) to execute the AVantage library call. Manages API key resolution, AVantage instance via resourceManager, invokes the specific method (av.commodities.wheat), 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}`
        );
      }
    }
  • Supporting schema definitions: DatatypeSchema and MonthlyQuarterlyAnnualSchema used in CommoditiesMonthlyQuarterlyAnnualParamsSchema.
    const DatatypeSchema = z.enum(['json', 'csv']).describe('Data format for the response.')
    const DailyWeeklyMonthlySchema = z.enum(['daily', 'weekly', 'monthly']).describe('Time interval.')
Behavior1/5

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. 'Retrieves' implies a read-only operation, but it doesn't specify critical behaviors such as data source, update frequency, rate limits, authentication requirements, or error handling. For a data retrieval tool with zero annotation coverage, this lack of behavioral 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 ('Retrieves wheat prices.') that is front-loaded and wastes no words. It directly conveys the core purpose without unnecessary elaboration, making it easy for an agent 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 complexity of a data retrieval tool with no annotations and no output schema, the description is incomplete. It lacks details on return values (e.g., price units, timestamps), data freshness, and potential limitations. While the schema covers parameters well, the overall context for effective tool use is insufficient, especially without behavioral transparency.

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 clear enum values for 'interval' and 'datatype'. The description adds no parameter semantics beyond what the schema provides—it doesn't explain default values, implications of choosing 'json' vs. 'csv', or how the interval affects the data retrieved. With high schema coverage, the baseline score of 3 is appropriate as 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 'Retrieves wheat prices' clearly states the verb ('retrieves') and resource ('wheat prices'), making the purpose immediately understandable. It distinguishes itself from sibling tools like commodities_corn or commodities_coffee by specifying wheat. However, it doesn't specify what kind of wheat prices (e.g., futures, spot, historical) or from which source, leaving some ambiguity compared to a perfect 5 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 sibling tools like commodities_globalIndex or other commodities, nor does it specify use cases (e.g., for historical analysis vs. real-time data). Without any context on alternatives or prerequisites, the agent must infer usage solely from the tool name and parameters.

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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