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MissionSquad

MCP Avantage

by MissionSquad

technicalIndicators_dema

Calculate Double Exponential Moving Average (DEMA) for financial analysis using stock symbols, time intervals, and price types to identify trends and generate trading signals.

Instructions

Double Exponential Moving Average (DEMA)

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.
series_typeYesThe desired price type.

Implementation Reference

  • src/index.ts:1028-1042 (registration)
    Registers the 'technicalIndicators_dema' MCP tool with name, description, input schema, and inline execute handler that delegates to executeAvantageTool and the AVantage library's dema method.
    server.addTool({
      name: "technicalIndicators_dema",
      description: "Double Exponential Moving Average (DEMA)",
      parameters: schemas.TechnicalIndicatorsTimeSeriesIndicatorParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "technicalIndicators_dema",
          args,
          context,
          (av, params) => av.technicalIndicators.dema(params)
        ),
    });
  • The execute handler function for the tool, which calls the generic executeAvantageTool helper passing the AVantage library's technicalIndicators.dema method.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool(
        "technicalIndicators_dema",
        args,
        context,
        (av, params) => av.technicalIndicators.dema(params)
      ),
  • Defines the Zod input parameters schema for the tool: symbol, interval, optional datatype/month, plus time_period and series_type.
    export const TechnicalIndicatorsTimeSeriesIndicatorParamsSchema = TechnicalIndicatorsCommonIndicatorParamsSchema.extend({
      time_period: z.string().describe('Number of data points used to calculate the indicator.'), // Using string as AV API expects string
      series_type: SeriesTypeSchema,
    }).describe('Parameters for time series based technical indicators.')
  • Generic helper function used by all tools (including this one) to resolve API key, manage AVantage client instance via resource manager, invoke the specified library method, handle errors, and return JSON string of 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}`
        );
      }
    }
  • Base schema extended by the tool's parameters schema, defining common fields: symbol, interval, datatype, month.
    export const TechnicalIndicatorsCommonIndicatorParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "IBM").'),
      interval: z.string().describe('Time interval (e.g., "daily", "60min", "weekly"). Check Alpha Vantage docs for valid intervals per indicator.'),
      datatype: DatatypeSchema.default('json').optional(),
      month: z.string().optional().describe('Specific month for intraday intervals (YYYY-MM format).'),
    }).describe('Common parameters for many technical indicators.')
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 provides none. It doesn't indicate whether this is a read-only operation, what data source it queries (Alpha Vantage), what the response format might be, whether there are rate limits, or any other behavioral characteristics. The description is completely silent on how the tool behaves when invoked.

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 technically concise (just the name expanded), this is under-specification rather than effective conciseness. The single phrase doesn't provide enough information to be helpful. Good conciseness balances brevity with completeness, but here the description is so minimal it fails to communicate basic purpose or usage.

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 technical indicator tool with 6 parameters (4 required) and no annotations or output schema, the description is completely inadequate. It doesn't explain what DEMA is, what it's used for, what data it returns, or how it differs from other technical indicators. The context signals show this is a moderately complex tool (6 parameters), but the description provides almost no contextual information to help an agent use it 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?

Schema description coverage is 100%, meaning all parameters are well-documented in the schema itself. The description adds no parameter information beyond what's already in the structured schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no parameter information in the description, which applies here.

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 'Double Exponential Moving Average (DEMA)' is essentially a tautology that restates the tool name without specifying what the tool actually does. It doesn't state that this calculates or retrieves DEMA values, nor does it mention that this is a technical indicator for financial data analysis. While the name suggests it's related to technical indicators, the description adds no meaningful clarification about the tool's function.

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 absolutely no guidance about when to use this tool versus alternatives. There are many sibling technical indicator tools (ema, sma, tema, etc.), but the description doesn't help an agent understand when DEMA is appropriate versus other moving averages or technical indicators. No context about use cases, prerequisites, or comparisons is provided.

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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