CG Alpha MCP

Instructions

Implementation Reference

• mcp-server.js:238-247 (handler)

  The MCP tool handler for 'ta_bollinger'. Validates input parameters (values array, optional period and mult), calls the bollinger helper function, and formats the response.

  "ta_bollinger": async (args) => {
    const values = Array.isArray(args?.values) ? args.values : null;
    const period = Number.isFinite(Number(args?.period)) ? Number(args.period) : 20;
    const mult   = Number.isFinite(Number(args?.mult))   ? Number(args.mult)   : 2;
    if (!values || values.length === 0) {
      return { content: textContent({ error:true, message:"'values' must be a non-empty array of numbers (oldest → newest)" }), isError:true };
    }
    const out = taBoll(values, period, mult);
    return { content: textContent({ ok:true, ...out, period, mult }) };
  },

• mcp-server.js:336-344 (registration)

  Tool registration entry in the tools list, including description, input schema, and annotations. Used by the MCP server's tools/list method.

  { name:"ta_bollinger",
    description:"Compute Bollinger Bands (SMA + population stdev). Inputs: values:number[] (oldest→newest), period?:number(20), mult?:number(2).",
    inputSchema:{ type:"object", properties:{
      values:{ type:"array", items:{ type:"number" } },
      period:{ type:"number" },
      mult:{ type:"number" }
    }, required:["values"] },
    annotations:{ title:"TA: Bollinger Bands", readOnlyHint:true, openWorldHint:false }
  },

• mcp-server.js:338-342 (schema)

  Input schema definition for the ta_bollinger tool, specifying the expected arguments.

  inputSchema:{ type:"object", properties:{
    values:{ type:"array", items:{ type:"number" } },
    period:{ type:"number" },
    mult:{ type:"number" }
  }, required:["values"] },

• services/ta.js:57-95 (helper)

  Core implementation of Bollinger Bands computation: calculates SMA mean, population stdev, upper/lower bands, and derived metrics (percentB, bandwidth) over the last 'period' values.

  export function bollinger(values, period = 20, mult = 2) {
    const arr = normalize(values);
    if (arr.length < period) return null;
  
    const slice = arr.slice(-period);
    const mean = avg(slice);
    if (!isFiniteNum(mean)) return null;
  
    const variance = avg(slice.map(v => (v - mean) * (v - mean)));
    const stdev = Math.sqrt(variance);
    if (!isFiniteNum(stdev)) return null;
  
    const upper = mean + mult * stdev;
    const lower = mean - mult * stdev;
    const last  = arr[arr.length - 1];
  
    // Avoid division by zero in derived metrics
    const denomBands = upper - lower;
    const denomMean  = mean;
  
    const percentB =
      isFiniteNum(denomBands) && denomBands !== 0
        ? (last - lower) / denomBands
        : null;
  
    const bandwidth =
      isFiniteNum(denomMean) && denomMean !== 0
        ? (upper - lower) / denomMean
        : null;
  
    return {
      mean,
      upper,
      lower,
      last,
      percentB: isFiniteNum(percentB) ? percentB : null,
      bandwidth: isFiniteNum(bandwidth) ? bandwidth : null
    };
  }