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MCP Avantage

by MissionSquad

alphaIntelligence_topGainersLosers

Identify top-performing and underperforming US stocks by retrieving daily gainers, losers, and most actively traded tickers for market analysis.

Instructions

Retrieves the top N gainers, losers, and most actively traded US tickers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • src/index.ts:138-153 (registration)
    Registers the MCP tool 'alphaIntelligence_topGainersLosers'. Specifies name, description, empty input schema (no parameters), and an execute handler that calls the generic 'executeAvantageTool' function passing a callback to invoke 'av.alphaIntelligence.topGainersLosers()' from the AVantage library.
    server.addTool({
      name: "alphaIntelligence_topGainersLosers",
      description:
        "Retrieves the top N gainers, losers, and most actively traded US tickers.",
      parameters: z.object({}), // No parameters needed
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "alphaIntelligence_topGainersLosers",
          args,
          context,
          (av) => av.alphaIntelligence.topGainersLosers()
        ),
    });
  • The execute handler for the tool, which delegates to the generic executeAvantageTool with the specific AVantage library method call 'av.alphaIntelligence.topGainersLosers()'.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool(
        "alphaIntelligence_topGainersLosers",
        args,
        context,
        (av) => av.alphaIntelligence.topGainersLosers()
      ),
  • Input schema definition using Zod: an empty object, indicating the tool takes no parameters.
    parameters: z.object({}), // No parameters needed
  • Generic helper function shared by all tools. Manages AVantage client instances via resourceManager, resolves API key from config or extraArgs, executes the provided avantageMethod callback on the AVantage instance, handles errors, and returns the JSON-stringified result 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. It states the tool 'retrieves' data, implying a read-only operation, but does not specify aspects like data freshness, rate limits, authentication needs, or error handling. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 front-loads the core functionality without any wasted words. It is appropriately sized for a tool with no parameters, making it easy to parse and understand quickly.

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 has no parameters and no output schema, the description is minimally complete. It covers what the tool does but lacks details on behavioral traits (e.g., data sources, limitations) and does not explain the return format. For a retrieval tool with no structured output documentation, more context would be helpful, but it meets the basic requirement.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by implying the tool returns a fixed set of categories (gainers, losers, most active) and is limited to US tickers, which provides semantic context beyond the empty schema. However, it does not clarify what 'top N' means (e.g., default value or range), leaving minor ambiguity.

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 tool's purpose: 'Retrieves the top N gainers, losers, and most actively traded US tickers.' It specifies the verb ('retrieves'), resource ('US tickers'), and scope ('top N gainers, losers, and most actively traded'). However, it does not explicitly differentiate from sibling tools like 'coreStock_bulkQuotes' or 'coreStock_daily', which might also retrieve ticker data, leaving some ambiguity about uniqueness.

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 does not mention any context, prerequisites, or exclusions, nor does it refer to sibling tools for comparison. This lack of usage instructions makes it harder for an AI agent to decide when this tool is appropriate.

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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