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MCP Avantage

by MissionSquad

fundamentalData_companyOverview

Retrieve comprehensive company overview data for any stock symbol to analyze financial fundamentals and make informed investment decisions.

Instructions

Fetches company overview details. Premium endpoint.

Input Schema

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

Implementation Reference

  • src/index.ts:776-790 (registration)
    Registers the MCP tool 'fundamentalData_companyOverview' with server.addTool(). Specifies description, input parameters schema from schemas.ts, and an inline execute handler that invokes the generic executeAvantageTool, passing a callback to call av.fundamentalData.companyOverview(params.symbol).
    server.addTool({
      name: "fundamentalData_companyOverview",
      description: "Fetches company overview details. Premium endpoint.",
      parameters: schemas.FundamentalDataSymbolParamsSchema,
      execute: (
        args,
        context // Let type be inferred
      ) =>
        executeAvantageTool(
          "fundamentalData_companyOverview",
          args,
          context,
          (av, params) => av.fundamentalData.companyOverview(params.symbol)
        ),
    });
  • Zod schema defining the input parameters for fundamental data tools like companyOverview: requires a 'symbol' string (e.g., 'IBM'). Used in multiple tool registrations.
    export const FundamentalDataSymbolParamsSchema = z.object({
      symbol: z.string().describe('The stock symbol (e.g., "IBM").'),
    }).describe('Parameter schema requiring only a stock symbol.')
  • Generic handler function shared across all tools. Manages authentication (API key from extraArgs or config), acquires AVantage instance via resourceManager, invokes the specific library method (e.g., av.fundamentalData.companyOverview), handles errors, and returns stringified data. This executes the core tool logic for 'fundamentalData_companyOverview'.
    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 the full burden of behavioral disclosure. It mentions 'Premium endpoint,' hinting at potential access restrictions or costs, but doesn't specify rate limits, authentication needs, error conditions, or what 'overview details' entail (e.g., fields returned, data freshness). This is insufficient for a tool with no annotation coverage.

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

Conciseness4/5

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

The description is brief and front-loaded with the core purpose in the first sentence. The second sentence adds useful context ('Premium endpoint') without redundancy. It could be slightly more structured but avoids unnecessary verbosity.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'company overview details' include (e.g., financial metrics, company description), potential limitations, or response format. For a tool fetching fundamental data, this leaves significant gaps for an agent to understand its behavior and outputs.

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 input schema has 100% description coverage, fully documenting the single 'symbol' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or examples. This meets the baseline score of 3 since the schema handles 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 ('company overview details'), making the purpose understandable. However, it doesn't differentiate this tool from sibling tools like 'fundamentalData_balanceSheet' or 'fundamentalData_incomeStatement' that also fetch fundamental data, leaving ambiguity about what specific overview details are provided.

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. It doesn't mention prerequisites (e.g., needing a valid stock symbol), exclusions, or comparisons to sibling tools like 'coreStock_quote' or 'fundamentalData_etfProfile', leaving the agent without context for selection.

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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