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zereight

Bithumb MCP Server

post_orders

Retrieve detailed order information for a specific cryptocurrency on Bithumb exchange, including order status, type, and transaction history.

Instructions

Get member's order details (Private)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
order_currencyYesOrder currency symbol
orderIdNoOrder ID (optional)
typeNoOrder type (bid or ask) (optional)
countNoNumber of orders to retrieve (optional, default: 100)
afterNoRetrieve orders after this timestamp (optional)

Implementation Reference

  • MCP server handler for the 'post_orders' tool: constructs IPostOrdersParams from request arguments and calls bithumbApi.postOrders.
    case 'post_orders':
       // Construct params object safely using if checks
       const orderParams: IPostOrdersParams = {
          order_currency: args.order_currency as string,
          // payment_currency removed
       };
       if (args.orderId) orderParams.orderId = args.orderId as string; // Corrected name
       if (args.type) orderParams.type = args.type as tradeType;
       if (args.count) orderParams.count = args.count as number;
       // Correctly handle 'after' as a number
       if (args.after !== undefined && args.after !== null) {
           const afterNum = Number(args.after); // Convert to number
           if (!isNaN(afterNum)) {
               orderParams.after = afterNum;
           } else {
               // Handle error: 'after' argument is not a valid number
               throw new McpError(ErrorCode.InvalidParams, "'after' argument must be a number.");
           }
       }
      result = await this.bithumbApi.postOrders(orderParams);
      break;
  • ApiBithumb class method that implements the postOrders API call to Bithumb's info/endpoint/orders using requestInfo.
    public async postOrders(params: IPostOrdersParams): Promise<IPostOrders> {
      const param = {
        ...params,
      };
      const res = <IPostOrders>await this.requestInfo('orders', param);
      return res;
    }
  • src/index.ts:167-181 (registration)
    Registers the 'post_orders' tool in the MCP server's tools list with name, description, and input schema.
    {
      name: 'post_orders',
      description: 'Get member\'s order details (Private)',
      inputSchema: {
        type: 'object',
        properties: {
          order_currency: { type: 'string', description: 'Order currency symbol' },
          // payment_currency removed as it's not in IPostOrdersParams
          orderId: { type: 'string', description: 'Order ID (optional)' }, // Corrected name
          type: { type: 'string', enum: ['bid', 'ask'], description: 'Order type (bid or ask) (optional)' },
          count: { type: 'number', description: 'Number of orders to retrieve (optional, default: 100)' },
          after: { type: 'number', description: 'Retrieve orders after this timestamp (optional)' } // Corrected type and description
        },
        required: ['order_currency'] // Only require order_currency now
      }
  • TypeScript interface defining the parameters for the postOrders tool (IPostOrdersParams).
    export interface IPostOrdersParams {
      orderId?: string;
      type?: 'bid' | 'ask';
      count?: number;
      after?: number;
      order_currency: string;
    }
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states it's 'Private', which hints at authentication needs, but doesn't elaborate on permissions, rate limits, response format, or whether it's a read/write operation. For a tool with no annotations, this is insufficient.

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 very concise with a single sentence, front-loaded with the core purpose. However, it could be more structured by including key usage notes, but it 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 no annotations and no output schema, the description is incomplete. It doesn't explain what 'Private' entails, the return format, or how to handle the optional parameters effectively. For a tool with 5 parameters and no structured context, more detail is needed.

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%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining relationships between parameters or usage examples. Baseline 3 is appropriate 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 verb ('Get') and resource ('member's order details'), making the purpose understandable. However, it doesn't distinguish this tool from sibling tools like 'post_order_detail' or 'get_transaction_history', which might also retrieve order-related information, so it misses full sibling differentiation.

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, context, or exclusions, leaving the agent to infer usage from the tool name and parameters 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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