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MissionSquad

MCP Avantage

by MissionSquad

fundamentalData_balanceSheet

Retrieve balance sheet data for stocks to analyze financial health and performance using Alpha Vantage's premium financial data.

Instructions

Fetches balance sheet data (annual/quarterly). Premium endpoint.

Input Schema

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

Implementation Reference

  • src/index.ts:854-869 (registration)
    Registers the MCP tool 'fundamentalData_balanceSheet' with the server, specifying name, description, input schema, and an inline execute handler that delegates to executeAvantageTool and the AVantage library's balanceSheet method.
    server.addTool({
      name: "fundamentalData_balanceSheet",
      description:
        "Fetches balance sheet data (annual/quarterly). Premium endpoint.",
      parameters: schemas.FundamentalDataSymbolParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "fundamentalData_balanceSheet",
          args,
          context,
          (av, params) => av.fundamentalData.balanceSheet(params.symbol)
        ),
    });
  • Defines the Zod input validation schema for the tool, requiring a single 'symbol' string parameter.
    export const FundamentalDataSymbolParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "IBM").'),
    }).describe('Parameter schema requiring only a stock symbol.')
  • Core helper function used by all tools (including this one) to manage Alpha Vantage API authentication, client lifecycle via ResourceManager, execute the specific AVantage library method, handle errors, and return JSON-stringified results.
    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 full burden. It mentions 'Premium endpoint,' indicating potential authentication or subscription needs, but lacks details on rate limits, error handling, data freshness, or output format. For a financial data tool with no annotations, this leaves significant behavioral gaps.

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 with two short phrases: 'Fetches balance sheet data (annual/quarterly). Premium endpoint.' Every word serves a purpose—specifying action, resource, granularity, and access level—with zero waste or redundancy.

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 no annotations, no output schema, and a financial data tool with potential complexity, the description is incomplete. It lacks details on response structure, error cases, authentication requirements, and how it differs from siblings. 'Premium endpoint' is a start but insufficient for full contextual understanding.

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 the parameter 'symbol' clearly documented in the schema. The description adds no additional parameter semantics beyond what the schema provides, such as symbol format examples or validation rules. Baseline 3 is appropriate given high schema coverage.

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: 'Fetches balance sheet data (annual/quarterly).' It specifies the verb ('fetches'), resource ('balance sheet data'), and temporal granularity. However, it doesn't explicitly differentiate from sibling tools like 'fundamentalData_incomeStatement' or 'fundamentalData_cashFlow', which would require a 5.

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 minimal guidance with 'Premium endpoint,' hinting at access restrictions, but offers no explicit when-to-use advice, alternatives, or exclusions. No comparison to sibling tools like 'fundamentalData_companyOverview' or 'fundamentalData_earnings' is provided, leaving usage context unclear.

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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