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MCP Avantage

by MissionSquad

technicalIndicators_natr

Calculate the Normalized Average True Range (NATR) to measure volatility for financial assets, providing risk assessment and volatility analysis for trading decisions.

Instructions

Normalized Average True Range (NATR)

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).
time_periodYesNumber of data points used to calculate the indicator.

Implementation Reference

  • src/index.ts:1255-1269 (registration)
    Registers the 'technicalIndicators_natr' MCP tool with the server, including its description, input schema, and execute handler that wraps the Alpha Vantage library call.
    server.addTool({
      name: "technicalIndicators_natr",
      description: "Normalized Average True Range (NATR)",
      parameters: schemas.TechnicalIndicatorsNatrParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "technicalIndicators_natr",
          args,
          context,
          (av, params) => av.technicalIndicators.natr(params)
        ),
    });
  • The execute handler function for the tool, which invokes executeAvantageTool to call the Alpha Vantage natr method with validated parameters.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool(
        "technicalIndicators_natr",
        args,
        context,
        (av, params) => av.technicalIndicators.natr(params)
      ),
  • Defines the Zod input validation schema for the tool, aliasing the time_period-only params schema for NATR.
    export const TechnicalIndicatorsNatrParamsSchema = TechnicalIndicatorsTimePeriodOnlyParamsSchema.describe('Parameters for NATR.');
  • Shared utility function used by all Alpha Vantage tools: manages authentication, client pooling via ResourceManager, executes the specific AVantage method, handles errors, and returns JSON 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 offers none. It doesn't indicate whether this is a read-only operation, what data source it uses (e.g., Alpha Vantage), potential rate limits, error conditions, or the format of the response. The description fails to describe any behavioral traits beyond the tool's name.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While the description is concise, it is under-specified rather than efficiently structured. A single phrase ('Normalized Average True Range (NATR)') fails to convey necessary information, making it ineffective. Conciseness should not come at the cost of clarity; here, the brevity results in a lack of useful content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

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 and no output schema or annotations, the description is severely incomplete. It doesn't explain what NATR is, how it's used, what the output contains, or any behavioral aspects. For a tool that likely returns financial data, this minimal description is inadequate to guide an AI agent 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 meaning about parameters, such as explaining the purpose of 'time_period' in the context of NATR or providing examples beyond what's in the schema. However, with high schema coverage, the baseline score is 3, as the schema adequately handles parameter documentation.

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

Purpose2/5

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

The description 'Normalized Average True Range (NATR)' is essentially a tautology that restates the tool name without explaining what the tool does. It doesn't specify the action (e.g., 'calculate', 'retrieve', or 'analyze') or the resource involved (e.g., financial data for a symbol). While it identifies the technical indicator, it fails to articulate the tool's function beyond naming it.

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 sibling tools (e.g., other technical indicators like ATR or BBANDS) or suggest contexts where NATR is appropriate (e.g., for volatility analysis). There's a complete absence of usage instructions, prerequisites, or exclusions.

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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