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MissionSquad

MCP Avantage

by MissionSquad

commodities_corn

Access corn commodity price data for monthly, quarterly, or annual intervals in JSON or CSV format to analyze market trends.

Instructions

Retrieves corn prices.

Input Schema

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

Implementation Reference

  • src/index.ts:274-285 (registration)
    Registration of the 'commodities_corn' MCP tool, including name, description, parameters schema, and inline execute handler that calls the Avantage library's commodities.corn method.
    server.addTool({
      name: "commodities_corn",
      description: "Retrieves corn prices.",
      parameters: schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_corn", args, context, (av, params) =>
          av.commodities.corn(params)
        ),
    });
  • The execute handler function for the tool, which delegates to the generic executeAvantageTool helper, ultimately calling av.commodities.corn(params) on the Avantage client instance.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool("commodities_corn", args, context, (av, params) =>
        av.commodities.corn(params)
      ),
  • Zod schema defining the input parameters for commodities tools like corn: 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.')
  • Core helper function shared across all tools: resolves API key, manages AVantage client lifecycle via resourceManager, executes the provided library method, 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 full burden. 'Retrieves' implies a read-only operation, but the description doesn't disclose important behavioral traits like whether this requires authentication, rate limits, data freshness, error conditions, or what format the prices come in (historical, current, futures). For a data retrieval tool with zero annotation coverage, this is insufficient.

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 extremely concise at just three words, front-loading the essential purpose with zero wasted language. Every word earns its place, making it efficient for an agent to parse.

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 incomplete. It doesn't explain what kind of corn prices are retrieved (spot, futures, historical), the data source, time ranges available, or what the return structure looks like. Given the complexity implied by the parameter enums and sibling tools, 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%, with both parameters (interval and datatype) well-documented with enum values and descriptions. The description doesn't add any parameter information beyond what the schema provides, which is acceptable given the high schema coverage. The baseline of 3 is appropriate 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 'Retrieves corn prices' clearly states the verb ('retrieves') and resource ('corn prices'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like commodities_wheat or commodities_sugar, which likely have similar retrieval patterns for different commodities.

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 for different commodities (wheat, sugar, coffee, etc.) and data types (coreStock, forex, crypto), there's no indication of when corn price retrieval is appropriate versus other commodity or financial data tools.

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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