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by MissionSquad

technicalIndicators_htTrendmode

Identify market trends versus cycles in financial data using the Hilbert Transform indicator to analyze price movements for trading decisions.

Instructions

Hilbert Transform - Trend vs Cycle Mode

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesThe stock symbol (e.g., "IBM").
intervalYesTime interval (e.g., "daily", "60min", "weekly"). Check Alpha Vantage docs for valid intervals per indicator.
datatypeNoData format for the response.json
monthNoSpecific month for intraday intervals (YYYY-MM format).
series_typeYesThe desired price type.

Implementation Reference

  • The registration and inline handler for the 'technicalIndicators_htTrendmode' tool. The execute function calls the shared 'executeAvantageTool' helper, which manages the Alpha Vantage client and invokes the specific 'av.technicalIndicators.htTrendmode(params)' method.
    server.addTool({
      name: "technicalIndicators_htTrendmode",
      description: "Hilbert Transform - Trend vs Cycle Mode",
      parameters: schemas.TechnicalIndicatorsHtTrendmodeParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "technicalIndicators_htTrendmode",
          args,
          context,
          (av, params) => av.technicalIndicators.htTrendmode(params)
        ),
    });
  • Zod schema defining the input parameters for the technicalIndicators_htTrendmode tool, extending the common indicator params with series_type.
    export const TechnicalIndicatorsHtTrendmodeParamsSchema = TechnicalIndicatorsCommonIndicatorParamsSchema.extend({ series_type: SeriesTypeSchema }).describe('Parameters for HT_TRENDMODE.');
  • Shared helper function used by all Alpha Vantage tools, including this one. It resolves the API key, manages AVantage client instances via resourceManager, calls the provided library method, 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}`
        );
      }
    }
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure but fails completely. It doesn't indicate whether this is a read-only operation, what data it returns, potential rate limits, or any side effects. For a financial data tool with 5 parameters, this lack of behavioral information is a critical gap that leaves the agent guessing about the tool's operation.

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 just 5 words, making it front-loaded and efficient. There's no wasted language or unnecessary elaboration. Every word serves to identify the technical indicator, though this brevity comes at the cost of completeness.

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 the complexity of a technical indicator tool with 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns, how to interpret the 'Trend vs Cycle Mode' output, or provide any context about the Hilbert Transform methodology. The agent would need to rely heavily on external knowledge to use this tool effectively.

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%, meaning all parameters are documented in the input schema. The description adds no additional parameter information beyond what's already in the schema. According to the scoring rules, when schema coverage is high (>80%), the baseline score is 3 even with no parameter details in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Hilbert Transform - Trend vs Cycle Mode' identifies the technical indicator but is vague about what the tool actually does. It names the indicator but doesn't specify the action (e.g., 'calculate', 'retrieve', or 'analyze') or what resource it operates on. Among sibling tools like 'technicalIndicators_sma' and 'technicalIndicators_ema', it doesn't clearly differentiate its specific purpose beyond the indicator name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

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. It doesn't mention any context, prerequisites, or comparisons to sibling tools like 'technicalIndicators_htTrendline' or other trend indicators. There's no indication of when this specific Hilbert Transform mode is appropriate, leaving the agent with no usage direction.

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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