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MissionSquad

MCP Avantage

by MissionSquad

commodities_coffee

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

Instructions

Retrieves coffee prices.

Input Schema

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

Implementation Reference

  • src/index.ts:313-324 (registration)
    Registers the 'commodities_coffee' MCP tool with name, description, input schema, and an execute handler that calls the generic executeAvantageTool wrapper with the specific AVantage library method av.commodities.coffee(params).
    server.addTool({
      name: "commodities_coffee",
      description: "Retrieves coffee prices.",
      parameters: schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_coffee", args, context, (av, params) =>
          av.commodities.coffee(params)
        ),
    });
  • Zod schema for input parameters of the commodities_coffee tool, allowing 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 helper function used by all tools, including commodities_coffee, to manage AVantage instance, resolve API key, execute the specific library method, handle responses and errors, and return 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. 'Retrieves' implies a read-only operation, but it doesn't specify whether this is a real-time or historical data source, if there are rate limits, authentication requirements, or what the output format looks like (beyond the 'datatype' parameter). For a data retrieval tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 coffee prices.') that is front-loaded with the core purpose. There is no wasted verbiage or redundancy, making it highly concise and well-structured for quick comprehension.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (2 parameters, no nested objects) and high schema coverage (100%), the description is minimally adequate. However, with no annotations and no output schema, it lacks details on behavioral traits (e.g., data freshness, error handling) and return values, which are important for a data retrieval tool. It meets the baseline but doesn't provide full context.

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 additional parameter information beyond what's in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

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 coffee prices' clearly states the verb ('retrieves') and resource ('coffee prices'), making the purpose immediately understandable. It distinguishes this tool from other commodity tools like 'commodities_aluminum' or 'commodities_corn' by specifying coffee. However, it doesn't explicitly differentiate from other price retrieval tools in the sibling list (e.g., 'coreStock_quote' for stocks), which keeps it from a perfect 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' for broader data or other commodity-specific tools, nor does it specify any prerequisites or constraints for usage. The agent must infer usage from the tool name and parameters alone.

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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