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MissionSquad

MCP Avantage

by MissionSquad

commodities_sugar

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

Instructions

Retrieves sugar prices.

Input Schema

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

Implementation Reference

  • Generic handler that executes all Avantage-based MCP tools, including commodities_sugar. Manages API key, AVantage instance via resourceManager, invokes the library method (av.commodities.sugar(params) for this tool), 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}`
        );
      }
    }
  • Input schema validation using Zod: optional 'interval' (monthly, quarterly, annual) and 'datatype' (json, csv). Used in the tool registration.
    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.')
  • src/index.ts:300-311 (registration)
    Registers the 'commodities_sugar' tool with the MCP server using server.addTool(). Specifies description, input schema, and execute handler that calls executeAvantageTool with the specific Avantage library method av.commodities.sugar(params).
    server.addTool({
      name: "commodities_sugar",
      description: "Retrieves sugar prices.",
      parameters: schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_sugar", args, context, (av, params) =>
          av.commodities.sugar(params)
        ),
    });
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 it doesn't disclose important behavioral traits like whether this requires authentication, rate limits, data freshness, source of the prices, or what format/scope the returned prices cover. The description is too minimal for a tool with no annotation support.

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, with zero wasted language. It's front-loaded with the core purpose and contains no unnecessary elaboration, 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?

Given the lack of annotations and output schema, the description is insufficiently complete. It doesn't explain what 'sugar prices' means (e.g., spot prices, futures, geographic scope), the data source, or what the return structure looks like. For a data retrieval tool with no structured output documentation, this leaves significant gaps.

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 enums and descriptions. The description adds no parameter information beyond what's in the schema, so it meets the baseline of 3 where 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 sugar prices' clearly states the verb ('Retrieves') and resource ('sugar prices'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'commodities_coffee' or 'commodities_corn' beyond specifying the commodity type, which is somewhat inherent in the tool name itself.

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 and data types, there's no indication of whether this is for historical prices, real-time data, or how it differs from other commodity tools in the server.

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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