Skip to main content
Glama
MissionSquad

MCP Avantage

by MissionSquad

commodities_copper

Retrieve copper price data in JSON or CSV format for monthly, quarterly, or annual intervals to analyze market trends and inform investment decisions.

Instructions

Retrieves copper prices.

Input Schema

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

Implementation Reference

  • src/index.ts:235-246 (registration)
    Registers the 'commodities_copper' MCP tool with server.addTool. Specifies the tool name, description, input validation schema, and an execute handler that uses the generic executeAvantageTool function to call the AVantage library's commodities.copper method.
    server.addTool({
      name: "commodities_copper",
      description: "Retrieves copper prices.",
      parameters: schemas.CommoditiesMonthlyQuarterlyAnnualParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("commodities_copper", args, context, (av, params) =>
          av.commodities.copper(params)
        ),
    });
  • Defines the Zod input schema for the commodities_copper tool, supporting 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 that implements the core execution logic for all AVantage-based MCP tools, including commodities_copper. Manages API authentication, AVantage instance caching, invokes the specific library method, 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}`
        );
      }
    }
  • Supporting enum schema used by CommoditiesMonthlyQuarterlyAnnualParamsSchema for the interval parameter.
    const MonthlyQuarterlyAnnualSchema = z.enum(['monthly', 'quarterly', 'annual']).describe('Time interval.')
  • Supporting enum schema used by CommoditiesMonthlyQuarterlyAnnualParamsSchema for the datatype parameter.
    const DatatypeSchema = z.enum(['json', 'csv']).describe('Data format for the response.')
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, 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 query, if there are rate limits, authentication requirements, or what the output format looks like (beyond the datatype parameter). The description is minimal and misses key behavioral details.

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—a single three-word sentence that directly states the tool's purpose. There is no wasted language, and it's front-loaded with the essential information. Every word earns its place, making it efficient 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 doesn't explain what the tool returns (e.g., price data structure, time series), behavioral aspects like error handling or data freshness, or how it fits within the broader commodity data context. For a tool with two parameters and 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?

The input schema has 100% description coverage, with clear enum values and descriptions for both parameters (interval and datatype). The description adds no additional parameter information beyond what's in the schema, so it meets the baseline of 3 for adequate coverage without extra value.

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 copper prices' clearly states the verb ('retrieves') and resource ('copper prices'), making the purpose immediately understandable. It distinguishes from siblings like 'commodities_aluminum' by specifying the commodity type, though it doesn't explicitly contrast with other copper-related tools (none exist in the sibling list).

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?

No guidance is provided on when to use this tool versus alternatives. While the name implies it's for copper prices specifically, there's no mention of how it differs from other commodity tools (e.g., frequency, data source, or use cases). The description lacks any context about prerequisites or typical scenarios for invocation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/MissionSquad/mcp-avantage'

If you have feedback or need assistance with the MCP directory API, please join our Discord server