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MCP Avantage

by MissionSquad

fundamentalData_splits

Retrieve historical stock split data for any company symbol to analyze corporate actions and adjust historical price calculations.

Instructions

Fetches historical stock split data for a symbol.

Input Schema

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

Implementation Reference

  • The execute handler specific to the fundamentalData_splits tool. It invokes the shared executeAvantageTool helper, passing the tool name, arguments, context, and a callback that calls the AlphaVantage library's fundamentalData.splits method with the symbol parameter.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool("fundamentalData_splits", args, context, (av, params) =>
        av.fundamentalData.splits(params.symbol)
      ),
  • src/index.ts:824-835 (registration)
    Registration of the fundamentalData_splits tool on the MCP server, including name, description, schema reference, and inline handler.
    server.addTool({
      name: "fundamentalData_splits",
      description: "Fetches historical stock split data for a symbol.",
      parameters: schemas.FundamentalDataSymbolParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("fundamentalData_splits", args, context, (av, params) =>
          av.fundamentalData.splits(params.symbol)
        ),
    });
  • Zod validation schema for the tool's input parameters, requiring a single 'symbol' string.
    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 authentication, create/reuse AVantage client instances via ResourceManager, execute the specific library method, handle responses/errors, and return 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?

No annotations are provided, so the description carries full burden. While 'fetches' implies a read operation, it doesn't disclose behavioral traits like whether this requires authentication, rate limits, what time period the data covers, or the format/structure of returned data. The description is minimal and lacks essential context for a data-fetching tool.

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 with zero waste. It's appropriately sized for a simple tool and front-loads the key information (fetches historical stock split data).

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 incomplete. It doesn't explain what 'historical' means (time range, frequency), what data format to expect, or any limitations/requirements. Given the complexity of financial data tools and lack of structured context, this description 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 description mentions 'for a symbol' which aligns with the single parameter in the schema. With 100% schema description coverage (the schema fully documents the 'symbol' parameter), the description adds minimal value beyond what's already in the structured data. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('fetches') and resource ('historical stock split data for a symbol'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'fundamentalData_dividends' or 'fundamentalData_earnings', which also fetch historical financial data for symbols.

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 (e.g., dividends, earnings, balance sheets), there's no indication that this tool is specifically for stock splits rather than other fundamental data.

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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