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MCP Avantage

by MissionSquad

alphaIntelligence_insiderTransactions

Fetch aggregated insider trading data for specific stock symbols to analyze corporate insider activity and inform investment decisions.

Instructions

Fetches aggregated insider trading information for a given symbol.

Input Schema

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

Implementation Reference

  • src/index.ts:155-170 (registration)
    Registers the 'alphaIntelligence_insiderTransactions' MCP tool with FastMCP server, including name, description, input parameters schema, and an execute handler that delegates to the generic executeAvantageTool function passing the specific AVantage library method.
    server.addTool({
      name: "alphaIntelligence_insiderTransactions",
      description:
        "Fetches aggregated insider trading information for a given symbol.",
      parameters: schemas.InsiderTransactionsParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "alphaIntelligence_insiderTransactions",
          args,
          context,
          (av, params) => av.alphaIntelligence.insiderTransactions(params.symbol)
        ),
    });
  • Zod schema defining the input parameters for the tool: requires a 'symbol' string parameter.
    export const InsiderTransactionsParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "AAPL").'),
    }).describe('Parameters for fetching insider trading information.')
  • Generic handler function that implements the core logic for all Alpha Vantage tools, including 'alphaIntelligence_insiderTransactions'. Manages authentication, AVantage instance via resourceManager, executes the provided library method (av.alphaIntelligence.insiderTransactions), handles 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?

No annotations are provided, so the description carries full burden. While 'fetches' implies a read operation, it doesn't disclose important behavioral traits like whether this requires authentication, rate limits, what time period the data covers, whether it's real-time or historical, or what format the aggregated information takes. The description is too minimal for a tool that presumably returns complex financial data.

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 gets straight to the point with no wasted words. It's appropriately sized for a single-parameter tool and front-loads the essential information.

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 financial data tool with no annotations and no output schema, the description is insufficient. It doesn't explain what 'aggregated insider trading information' includes, the data format, time coverage, or any limitations. Given the complexity of insider trading data and the lack of structured output documentation, the description should provide more context about what the tool actually returns.

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 'symbol' parameter well-documented in the schema. The description adds no additional parameter information beyond what's already in the schema ('for a given symbol'), so it meets the baseline score of 3 for 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 action ('fetches') and resource ('aggregated insider trading information for a given symbol'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'alphaIntelligence_newsSentiments' or 'alphaIntelligence_topGainersLosers' which are also Alpha Intelligence tools, so it misses the highest score.

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 available (including other Alpha Intelligence tools and various financial data tools), there's no indication of when insider trading data is appropriate versus other data types like news sentiments or technical indicators.

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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