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MCP Avantage

by MissionSquad

fundamentalData_earnings

Fetch annual and quarterly earnings data for stocks to analyze company financial performance and investment opportunities.

Instructions

Fetches earnings data (annual/quarterly). Premium endpoint.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesThe stock symbol (e.g., "IBM").

Implementation Reference

  • src/index.ts:887-901 (registration)
    Registers the 'fundamentalData_earnings' MCP tool, specifying its description, input schema, and execute handler that invokes the shared executeAvantageTool helper with the specific Avantage library method av.fundamentalData.earnings(symbol).
    server.addTool({
      name: "fundamentalData_earnings",
      description: "Fetches earnings data (annual/quarterly). Premium endpoint.",
      parameters: schemas.FundamentalDataSymbolParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "fundamentalData_earnings",
          args,
          context,
          (av, params) => av.fundamentalData.earnings(params.symbol)
        ),
    });
  • Zod schema defining the input parameters for the tool: a required 'symbol' string (e.g., stock ticker like 'IBM'). Used in the tool registration.
    export const FundamentalDataSymbolParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "IBM").'),
    }).describe('Parameter schema requiring only a stock symbol.')
  • The execute handler function for the tool, which calls executeAvantageTool to perform the actual API call to Alpha Vantage's earnings endpoint using the provided symbol.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool(
        "fundamentalData_earnings",
        args,
        context,
        (av, params) => av.fundamentalData.earnings(params.symbol)
      ),
  • Shared helper function used by all MCP tools to execute Alpha Vantage library methods: resolves API key from context or config, acquires/manages AVantage client instance via resource manager, invokes the specific method, handles responses/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?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions 'Premium endpoint' which suggests potential authentication or subscription requirements, but doesn't specify rate limits, data freshness, pagination, error conditions, or what 'earnings data' actually includes beyond annual/quarterly granularity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise with just two brief phrases. While efficient, it might be too minimal given the lack of annotations and sibling tool context. Every word earns its place, but more context could be beneficial.

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 tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'earnings data' includes (EPS, revenue, guidance?), doesn't specify time periods covered, doesn't mention data format or structure, and provides minimal behavioral context despite being a data-fetching tool in a crowded namespace.

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 schema description coverage is 100% and clearly documents the single required 'symbol' parameter. The description adds no additional parameter semantics beyond what the schema already provides, so it meets the baseline expectation when schema coverage is complete.

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 fetches earnings data with annual/quarterly granularity, which is a specific verb+resource combination. However, it doesn't distinguish itself from sibling tools like fundamentalData_incomeStatement or fundamentalData_cashFlow that might also provide financial data, so it doesn't fully differentiate from alternatives.

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 like fundamentalData_incomeStatement or fundamentalData_companyOverview. The mention of 'Premium endpoint' hints at potential access restrictions but doesn't provide explicit usage context or exclusions.

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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