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MCP Avantage

by MissionSquad

commodities_brentCrudeOil

Retrieve Brent crude oil price data for daily, weekly, or monthly intervals in JSON or CSV format to analyze energy market trends.

Instructions

Retrieves Brent crude oil prices.

Input Schema

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

Implementation Reference

  • src/index.ts:206-220 (registration)
    Registers the 'commodities_brentCrudeOil' MCP tool, defining its name, description, input schema, and execute handler that wraps the Avantage library call av.commodities.brentCrudeOil(params) using the generic executeAvantageTool.
    server.addTool({
      name: "commodities_brentCrudeOil",
      description: "Retrieves Brent crude oil prices.",
      parameters: schemas.CommoditiesDailyWeeklyMonthlyParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "commodities_brentCrudeOil",
          args,
          context,
          (av, params) => av.commodities.brentCrudeOil(params)
        ),
    });
  • Zod schema defining optional 'interval' (daily/weekly/monthly) and 'datatype' (json/csv) input parameters for the commodities_brentCrudeOil tool.
    export const CommoditiesDailyWeeklyMonthlyParamsSchema = z.object({
      interval: DailyWeeklyMonthlySchema.optional().describe('Time interval for the data.'),
      datatype: DatatypeSchema.optional().describe('Response data format.'),
    }).describe('Parameters for daily/weekly/monthly commodity data.')
  • Generic handler function executed by the tool's 'execute' callback. Manages authentication, AVantage instance via resourceManager, invokes the specific library method (av.commodities.brentCrudeOil), 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-only operation, the description doesn't mention authentication requirements, rate limits, data freshness, error conditions, or what the response looks like (structure, units, etc.). For a data retrieval tool with zero annotation coverage, this leaves significant behavioral gaps.

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 zero wasted words. It's appropriately sized for a simple data retrieval tool and front-loads the core purpose immediately.

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 format the prices are returned in (numerical values, time series, etc.), what time periods are available, whether it's real-time or historical data, or any limitations. The agent would need to guess or trial-and-error to understand the full behavior.

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 and descriptions for both parameters. The description adds no parameter information beyond what's in the schema, which is acceptable given the high schema coverage. The baseline score of 3 reflects that the schema does the heavy lifting for parameter documentation.

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 Brent crude oil prices' clearly states the verb ('retrieves') and resource ('Brent crude oil prices'), making the purpose immediately understandable. However, it doesn't distinguish this tool from its sibling 'commodities_wtiCrudeOil' (which presumably retrieves WTI crude oil prices), nor does it mention the time interval or data format capabilities that differentiate it from other commodity tools.

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 the sibling 'commodities_wtiCrudeOil' for comparison, nor does it indicate whether this is for historical or current prices, or any prerequisites for use. The agent must infer usage from the tool name 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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