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MissionSquad

MCP Avantage

by MissionSquad

fundamentalData_dividends

Retrieve historical dividend data for stocks to analyze company payout history and investor returns.

Instructions

Fetches historical dividend data for a symbol.

Input Schema

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

Implementation Reference

  • src/index.ts:808-822 (registration)
    Registration of the 'fundamentalData_dividends' MCP tool using server.addTool. Specifies name, description, input parameters schema, and an execute handler that invokes the generic executeAvantageTool wrapper around the AVantage library's fundamentalData.dividends method.
    server.addTool({
      name: "fundamentalData_dividends",
      description: "Fetches historical dividend data for a symbol.",
      parameters: schemas.FundamentalDataSymbolParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "fundamentalData_dividends",
          args,
          context,
          (av, params) => av.fundamentalData.dividends(params.symbol)
        ),
    });
  • Zod input schema definition for the tool, requiring a single 'symbol' parameter (stock ticker).
    export const FundamentalDataSymbolParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "IBM").'),
    }).describe('Parameter schema requiring only a stock symbol.')
  • Shared helper function that implements the core execution logic for the tool: resolves API key, manages AVantage client instance via resourceManager, calls the library method (av.fundamentalData.dividends), handles responses and errors.
    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. While 'fetches historical dividend data' implies a read-only operation, it doesn't specify important behavioral aspects like data format, time range coverage, frequency of updates, rate limits, authentication requirements, or error conditions. The description is minimal and lacks operational context.

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 sentence that gets straight to the point with no wasted words. It's front-loaded with the core functionality and uses clear, direct language without 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?

For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the dividend data includes (amounts, dates, types), the historical range available, the format of returned data, or any limitations. Given the complexity of financial data and the lack of structured output information, the description should provide more operational context.

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 single parameter 'symbol' clearly documented in the schema as 'The stock symbol (e.g., "IBM")'. The description adds no additional parameter information beyond what's already in the schema, which is acceptable given the complete schema coverage but doesn't provide 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 clearly states the action ('fetches') and resource ('historical dividend data for a symbol'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'fundamentalData_earnings' or 'fundamentalData_splits' which also fetch historical financial data for symbols, leaving room for ambiguity in tool selection.

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 fetching different types of financial data (earnings, splits, balance sheets, etc.), there's no indication of when dividends specifically are needed or what distinguishes this tool from other fundamental data tools.

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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