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Cognitive-Stack

Volume Wall Detector MCP

fetch-order-book

Retrieve real-time order book data for a specific stock symbol to analyze market depth and liquidity using the Volume Wall Detector MCP server.

Instructions

Fetch current order book data for a symbol

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesStock symbol to fetch order book for

Implementation Reference

  • The execute function serving as the tool handler, which fetches the order book data using fetchOrderBook, stores it in MongoDB, and returns the result as JSON.
    execute: async (args) => {
      const orderBook = await fetchOrderBook(args.symbol);
      const result = await storeStockData(orderBook, "order_books");
      return JSON.stringify(result);
    }
  • Zod input schema defining the 'symbol' parameter for the tool.
    parameters: z.object({
      symbol: z.string().describe("Stock symbol to fetch order book for")
    }),
  • Tool configuration object for 'fetch-order-book' added to the tools array.
    {
      name: "fetch-order-book",
      description: "Fetch current order book data for a symbol",
      parameters: z.object({
        symbol: z.string().describe("Stock symbol to fetch order book for")
      }),
      execute: async (args) => {
        const orderBook = await fetchOrderBook(args.symbol);
        const result = await storeStockData(orderBook, "order_books");
        return JSON.stringify(result);
      }
    },
  • src/index.ts:16-19 (registration)
    Registers all tools from services/tools.ts to the FastMCP server instance.
    // Register all tools
    tools.forEach((tool) => {
      (server.addTool as Tool)(tool);
    });
  • Core helper function that performs the API request to fetch order book data and maps it to the OrderBook type.
    export const fetchOrderBook = async (symbol: string): Promise<OrderBook> => {
      const url = `${config.API_BASE_URL}/v2/stock/${symbol}`;
      const response = await axios.get(url, { headers });
      
      const data = response.data.data;
      return {
        symbol,
        timestamp: new Date().toISOString(),
        match_price: data.mp,
        bid_1: {
          price: data.b1,
          volume: data.b1v
        },
        ask_1: {
          price: data.o1,
          volume: data.o1v
        },
        change_percent: data.lpcp,
        volume: data.lv
      };
    };
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 states the tool fetches data, implying a read-only operation, but doesn't specify critical details like rate limits, authentication requirements, data freshness, or error handling. This leaves significant gaps in understanding the tool's behavior.

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 is front-loaded with the core action and resource, making it highly concise and well-structured for quick comprehension.

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 insufficiently complete. It doesn't explain what the order book data includes (e.g., bid/ask prices, depths), how it's formatted, or any limitations, leaving the agent with incomplete context for effective use.

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, with the 'symbol' parameter clearly documented as 'Stock symbol to fetch order book for'. The description adds no additional parameter semantics beyond this, so it meets the baseline for high schema coverage without compensating value.

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 ('fetch') and resource ('current order book data for a symbol'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'analyze-stock' or 'fetch-trades', which might also involve stock data retrieval, preventing 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 'analyze-stock' or 'fetch-trades'. It lacks context about specific use cases, prerequisites, or exclusions, leaving the agent to infer usage 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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