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MCP Avantage

by MissionSquad

commodities_naturalGas

Retrieve natural gas price data for daily, weekly, or monthly intervals in JSON or CSV format to analyze market trends.

Instructions

Retrieves natural gas prices.

Input Schema

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

Implementation Reference

  • src/index.ts:222-233 (registration)
    Registers the 'commodities_naturalGas' MCP tool, specifying its name, description, input schema, and execute handler that invokes the AVantage library's commodities.naturalGas method via the generic executeAvantageTool wrapper.
    server.addTool({
      name: "commodities_naturalGas",
      description: "Retrieves natural gas prices.",
      parameters: schemas.CommoditiesDailyWeeklyMonthlyParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_naturalGas", args, context, (av, params) =>
          av.commodities.naturalGas(params)
        ),
    });
  • Defines the Zod input validation schema for the commodities_naturalGas tool (and similar), supporting optional 'interval' (daily/weekly/monthly) and 'datatype' (json/csv) parameters.
    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 used by commodities_naturalGas (and all tools) to manage AVantage client lifecycle, execute the library method av.commodities.naturalGas(params), handle errors, and return 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}`
        );
      }
    }
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. 'Retrieves' implies a read-only operation, but it doesn't disclose behavioral traits like whether this requires authentication, rate limits, data freshness, error handling, or what the output looks like (since no output schema exists). 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 a single, efficient sentence with zero wasted words. It's front-loaded with the core purpose ('Retrieves natural gas prices'), making it immediately clear. Every word earns its place, and there's no unnecessary elaboration.

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 complexity (a data retrieval tool with no annotations and no output schema), the description is incomplete. It doesn't explain what the tool returns (e.g., price values, timestamps, units), any prerequisites, or how it fits within the broader commodity data context. With 100% schema coverage but missing behavioral and output details, it falls short.

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') fully documented in the schema with enums and descriptions. The description adds no parameter semantics beyond what the schema provides, 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 clearly states the verb 'retrieves' and the resource 'natural gas prices', making the purpose immediately understandable. It distinguishes itself from siblings like 'commodities_aluminum' or 'commodities_coffee' by specifying the commodity type. However, it doesn't specify what kind of prices (e.g., spot, futures, historical) or from which source, which prevents 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. With many sibling tools for different commodities (e.g., 'commodities_brentCrudeOil', 'commodities_copper'), there's no indication of whether this is for current prices, historical data, or how it differs from other commodity tools. The agent must infer usage from the 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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