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MissionSquad

MCP Avantage

by MissionSquad

crypto_monthly

Fetch monthly cryptocurrency time series data for analysis, providing historical price information in JSON or CSV format based on symbol and market parameters.

Instructions

Fetches monthly time series data for a cryptocurrency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesThe cryptocurrency symbol (e.g., "BTC").
marketYesThe exchange market (e.g., "USD", "EUR").
datatypeNoData format for the response.json

Implementation Reference

  • The inline execute handler for the 'crypto_monthly' MCP tool. It delegates to the shared executeAvantageTool helper, which calls the Alpha Vantage crypto.monthly API method.
    execute: (
      args,
      context // Let type be inferred
    ) =>
      executeAvantageTool("crypto_monthly", args, context, (av, params) =>
        av.crypto.monthly(params)
      ),
  • Zod schema defining input parameters for crypto time series tools (daily/weekly/monthly), including symbol, market, and optional datatype.
    export const CryptoTimeSeriesParamsSchema = z.object({
      symbol: z.string().describe('The cryptocurrency symbol (e.g., "BTC").'),
      market: z.string().describe('The exchange market (e.g., "USD", "EUR").'),
      datatype: DatatypeSchema.default('json').optional(),
    }).describe('Parameters for fetching daily/weekly/monthly crypto time series data.')
  • src/index.ts:537-548 (registration)
    MCP server tool registration for 'crypto_monthly', specifying name, description, input schema, and handler.
    server.addTool({
      name: "crypto_monthly",
      description: "Fetches monthly time series data for a cryptocurrency.",
      parameters: schemas.CryptoTimeSeriesParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool("crypto_monthly", args, context, (av, params) =>
          av.crypto.monthly(params)
        ),
    });
  • Shared utility function used by all tools to manage Alpha Vantage client instances, perform API calls via the provided method, handle authentication, errors, and return JSON string responses.
    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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states it 'fetches' data (implying read-only), but doesn't mention authentication requirements, rate limits, data freshness, or what the response structure looks like. For a data-fetching tool with zero annotation coverage, this leaves significant behavioral gaps.

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 directly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the essential information.

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

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (3 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks details about response format, data granularity, or how it differs from similar tools. With no output schema, the description should ideally mention what kind of data structure to expect.

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%, with all parameters well-documented in the input schema. The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline score 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 action ('fetches') and resource ('monthly time series data for a cryptocurrency'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'crypto_daily', 'crypto_weekly', or 'coreStock_monthly', which would require explicit comparison to achieve a perfect 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 like 'crypto_daily' or 'crypto_weekly' for different timeframes, or 'coreStock_monthly' for non-crypto assets. Without any contextual usage instructions, the agent must infer based on tool names alone.

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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