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questflowai

Aster Finance MCP Server

by questflowai

klines

Retrieve candlestick chart data for cryptocurrency trading symbols to analyze price movements and market trends over specified time intervals.

Instructions

Get Kline/candlestick bars for a symbol.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endTimeNoEnd time in ms
intervalYesKline interval (e.g., 1m, 5m, 1h, 1d)
limitNoNumber of results. Default 500, max 1500.
startTimeNoStart time in ms
symbolYesTrading symbol

Implementation Reference

  • Handler for the 'klines' tool. Dispatches the tool call by making a GET request to the '/fapi/v1/klines' endpoint on the AsterDex API using the makeRequest utility.
    case 'klines':
      return makeRequest('GET', '/fapi/v1/klines', args);
  • Schema definition and registration of the 'klines' tool in the listTools response, including input schema with required symbol and interval parameters.
    {
      name: 'klines',
      description: 'Get Kline/candlestick bars for a symbol.',
      inputSchema: {
        type: 'object',
        properties: {
          symbol: { type: 'string', description: 'Trading symbol' },
          interval: { type: 'string', description: 'Kline interval (e.g., 1m, 5m, 1h, 1d)' },
          startTime: { type: 'number', description: 'Start time in ms' },
          endTime: { type: 'number', description: 'End time in ms' },
          limit: { type: 'number', description: 'Number of results. Default 500, max 1500.' },
        },
        required: ['symbol', 'interval'],
      },
    },
  • Shared helper function makeRequest that performs HTTP requests to the AsterDex API, handles signing for authenticated endpoints, and formats responses 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;
      }
    };
    
    switch (name) {
      // Market Data Endpoints
      case 'ping':
        return makeRequest('GET', '/fapi/v1/ping', {});
      case 'time':
        return makeRequest('GET', '/fapi/v1/time', {});
      case 'exchangeInfo':
        return makeRequest('GET', '/fapi/v1/exchangeInfo', {});
      case 'depth':
        return makeRequest('GET', '/fapi/v1/depth', args);
      case 'trades':
        return makeRequest('GET', '/fapi/v1/trades', args);
      case 'historicalTrades':
        return makeRequest('GET', '/fapi/v1/historicalTrades', args);
      case 'aggTrades':
        return makeRequest('GET', '/fapi/v1/aggTrades', args);
      case 'klines':
        return makeRequest('GET', '/fapi/v1/klines', args);
      case 'indexPriceKlines':
          return makeRequest('GET', '/fapi/v1/indexPriceKlines', args);
      case 'markPriceKlines':
          return makeRequest('GET', '/fapi/v1/markPriceKlines', args);
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 states the tool 'gets' data, implying a read-only operation, but doesn't mention any behavioral traits like rate limits, authentication requirements, data freshness, or error conditions. For a financial data tool with no annotation coverage, this is a significant gap.

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 front-loads the core purpose without any wasted words. It's appropriately sized for a straightforward data retrieval tool, making it easy to parse quickly.

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 complexity of financial data retrieval, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the returned candlestick bars contain (e.g., OHLCV data), how results are structured, or any limitations beyond what's implied by parameters. For a tool with 5 parameters in this domain, more context 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?

The description doesn't add any parameter semantics beyond what's already in the input schema, which has 100% coverage with clear descriptions for all 5 parameters. The baseline score of 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate with additional context like parameter interactions or examples.

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 ('Get') and resource ('Kline/candlestick bars for a symbol'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'indexPriceKlines' or 'markPriceKlines', which appear to serve similar functions for different data types, leaving some ambiguity about when to choose this specific tool.

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 siblings like 'indexPriceKlines' and 'markPriceKlines' that seem related, there's no indication of what distinguishes this tool (e.g., regular market data vs. index or mark price data). No context or exclusions are mentioned.

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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