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MCP Avantage

by MissionSquad

commodities_wtiCrudeOil

Retrieve West Texas Intermediate crude oil price data in daily, weekly, or monthly intervals to analyze market trends and inform trading decisions.

Instructions

Retrieves West Texas Intermediate (WTI) crude oil prices.

Input Schema

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

Implementation Reference

  • src/index.ts:190-204 (registration)
    Registration of the 'commodities_wtiCrudeOil' tool using server.addTool. Specifies the tool name, description, input parameters schema, and an execute handler that invokes the generic executeAvantageTool function with the specific AVantage library method av.commodities.wtiCrudeOil.
    server.addTool({
      name: "commodities_wtiCrudeOil",
      description: "Retrieves West Texas Intermediate (WTI) crude oil prices.",
      parameters: schemas.CommoditiesDailyWeeklyMonthlyParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "commodities_wtiCrudeOil",
          args,
          context,
          (av, params) => av.commodities.wtiCrudeOil(params)
        ),
    });
  • Zod schema definition for input parameters of the commodities_wtiCrudeOil tool, supporting optional interval (daily/weekly/monthly) and datatype (json/csv).
    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 helper function that implements the core execution logic for all AVantage-based MCP tools, including authentication, resource management, API call via provided method, and response processing. Used by commodities_wtiCrudeOil via the specific avantageMethod lambda.
    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. It states the tool retrieves data, implying a read-only operation, but does not specify details like data sources, update frequency, rate limits, authentication needs, or error handling. This lack of information leaves significant gaps in understanding the tool's 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 that directly states the tool's purpose without unnecessary words. It is front-loaded and wastes no space, making it highly concise and well-structured for quick understanding.

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 incomplete. It does not cover behavioral aspects like data retrieval specifics, potential limitations, or return value details, which are crucial for a data-fetching tool. The high schema coverage helps with parameters, but overall context is insufficient for effective tool use.

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 does not add any parameter semantics beyond what the schema provides, such as explaining the implications of choosing different intervals or formats. Since schema coverage is high, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 clearly states the verb ('Retrieves') and resource ('West Texas Intermediate (WTI) crude oil prices'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'commodities_brentCrudeOil', which retrieves a different type of crude oil price, leaving some ambiguity in sibling distinction.

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, such as other commodity tools or data formats. There is no mention of use cases, prerequisites, or comparisons with siblings like 'commodities_brentCrudeOil' or tools with different intervals, leaving the agent without contextual usage instructions.

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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