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questflowai

Aster Finance MCP Server

by questflowai

trades

Retrieve recent cryptocurrency market trades data from Aster Finance exchange for trading analysis and market monitoring.

Instructions

Get recent market trades.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoNumber of results. Default 500, max 1000.
symbolYesTrading symbol

Implementation Reference

  • Switch case that handles execution of the 'trades' tool by calling makeRequest to the AsterDex API /fapi/v1/trades endpoint.
    case 'trades':
      return makeRequest('GET', '/fapi/v1/trades', args);
  • src/index.ts:67-78 (registration)
    Registration of the 'trades' tool within the ListToolsRequestHandler response, defining name, description, and input schema.
    {
      name: 'trades',
      description: 'Get recent market trades.',
      inputSchema: {
        type: 'object',
        properties: {
          symbol: { type: 'string', description: 'Trading symbol' },
          limit: { type: 'number', description: 'Number of results. Default 500, max 1000.' },
        },
        required: ['symbol'],
      },
    },
  • JSON Schema defining the input parameters for the 'trades' tool: required symbol (string), optional limit (number).
    inputSchema: {
      type: 'object',
      properties: {
        symbol: { type: 'string', description: 'Trading symbol' },
        limit: { type: 'number', description: 'Number of results. Default 500, max 1000.' },
      },
      required: ['symbol'],
    },
  • Utility function makeRequest that performs the actual API call, handles signing for authenticated endpoints, and formats the response for MCP.
    const makeRequest = async (method: 'GET' | 'POST' | 'DELETE', path: string, params: any, isSigned = false) => {
      try {
        let config: any = {
          method,
          url: path,
        };
    
        if (isSigned) {
          if (!API_KEY || !API_SECRET) {
            throw new McpError(ErrorCode.InvalidRequest, 'API_KEY and API_SECRET must be configured.');
          }
          params.timestamp = Date.now();
          const queryString = new URLSearchParams(params).toString();
          const signature = crypto.createHmac('sha256', API_SECRET).update(queryString).digest('hex');
          params.signature = signature;
          
          config.headers = { 'X-MBX-APIKEY': API_KEY };
        }
        
        if (method === 'GET' || method === 'DELETE') {
          config.params = params;
        } else { // POST
          config.data = new URLSearchParams(params).toString();
          config.headers = { ...config.headers, 'Content-Type': 'application/x-www-form-urlencoded' };
        }
    
        const response = await this.axiosInstance.request(config);
        return { content: [{ type: 'text', text: JSON.stringify(response.data, null, 2) }] };
      } catch (error) {
        if (axios.isAxiosError(error)) {
          throw new McpError(
            ErrorCode.InternalError,
            `Aster API error: ${error.response?.data?.msg || error.message}`
          );
        }
        throw error;
      }
    };
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. 'Get recent market trades' implies a read operation but doesn't specify whether this requires authentication, has rate limits, returns real-time or delayed data, or what format the trades come in. For a financial data tool with zero annotation coverage, 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.

Conciseness5/5

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

The description is a single, efficient sentence that communicates the core purpose without any wasted words. It's appropriately sized for a simple data retrieval tool and front-loads the essential information.

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 financial market data tool with no annotations and no output schema, the description is inadequate. It doesn't explain what 'recent' means (timeframe), what data fields are returned, whether authentication is required, or how this differs from similar sibling tools. The context demands more completeness.

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%, with both parameters ('limit' and 'symbol') well-documented in the schema. The description doesn't add any parameter information beyond what's already in the schema, so it meets the baseline for high schema coverage but doesn't provide additional context.

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 'Get recent market trades' clearly states the verb ('Get') and resource ('recent market trades'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'historicalTrades' or 'getTradeList', which appear to serve similar functions.

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. With multiple sibling tools that might retrieve trade data (historicalTrades, getTradeList, aggTrades), there's no indication of what makes 'trades' distinct or when it should be preferred.

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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