generate_distribution

Instructions

Generate random numbers according to specified probability distribution type and parameters.
Supports various common probability distributions.

Args:
    distribution_type (int): Distribution type:
        1 = Uniform distribution (parameters: [min_value, max_value])
        2 = Normal distribution (parameters: [mean, standard_deviation])
        3 = Exponential distribution (parameters: [scale_parameter])
        4 = Binomial distribution (parameters: [trials, success_probability])
    distribution_parameters (List[float]): Distribution parameter list
    salt (str, optional): Random number salt value for increased randomness. Defaults to "".

Returns:
    str: JSON string containing random value and distribution information, formatted as:
    {
        "requestId": "Generated request ID",
        "randomValue": Generated random value,
        "distributionMetadata": {
            "distributionType": Distribution type,
            ...Distribution parameters
        }
    }

Application Scenarios:
1. Financial market simulation (return distribution, risk analysis)
2. Natural phenomena simulation (particle distribution, noise generation)
3. Load testing (user behavior distribution)
4. Statistical sampling (experimental data generation)

Implementation Reference

• main.py:142-175 (handler)

  MCP tool registration and handler wrapper for generate_distribution. Delegates to the distribution_generator utility function.

  @mcp.tool()
  async def generate_distribution(distribution_type: int, distribution_parameters: List[float], salt: str = "") -> str:
      """Probability Distribution Random Generator
  
      Generate random numbers according to specified probability distribution type and parameters.
      Supports various common probability distributions.
  
      Args:
          distribution_type (int): Distribution type:
              1 = Uniform distribution (parameters: [min_value, max_value])
              2 = Normal distribution (parameters: [mean, standard_deviation])
              3 = Exponential distribution (parameters: [scale_parameter])
              4 = Binomial distribution (parameters: [trials, success_probability])
          distribution_parameters (List[float]): Distribution parameter list
          salt (str, optional): Random number salt value for increased randomness. Defaults to "".
  
      Returns:
          str: JSON string containing random value and distribution information, formatted as:
          {
              "requestId": "Generated request ID",
              "randomValue": Generated random value,
              "distributionMetadata": {
                  "distributionType": Distribution type,
                  ...Distribution parameters
              }
          }
  
      Application Scenarios:
      1. Financial market simulation (return distribution, risk analysis)
      2. Natural phenomena simulation (particle distribution, noise generation)
      3. Load testing (user behavior distribution)
      4. Statistical sampling (experimental data generation)
      """
      return await distribution_generator(distribution_type, distribution_parameters, salt)

• utils.py:352-408 (helper)

  Core implementation of the distribution generator using NumPy for uniform, normal, exponential, and binomial distributions. Derives seed from blockchain random source.

  async def distribution_generator(distribution_type: int, distribution_parameters: List[float], salt: str="") -> Dict:
      """
      Distribution random generator
      
      Generate random numbers following specified probability distribution
      
      Args:
          distribution_type: Type of distribution:
              1 = Uniform distribution
              2 = Normal distribution
              3 = Exponential distribution
              4 = Binomial distribution
          distribution_parameters: Parameters for the distribution
          salt: Optional salt value for additional randomness
          
      Returns:
          Dict containing random value and distribution metadata
      """
      random_num = await get_random_str()
      if not random_num:
          return {"error": "Failed to get random number"}
      request_id = generate_request_id(random_num)
      seed = _derive_seed(request_id, salt)
      np.random.seed(seed)
      
      metadata = {"distributionType": distribution_type}
      
      if distribution_type == 1:  # Uniform distribution
          min_val, max_val = distribution_parameters
          random_value = float(np.random.uniform(min_val, max_val))
          metadata.update({"min": min_val, "max": max_val})
          
      elif distribution_type == 2:  # Normal distribution
          mean, std_dev = distribution_parameters
          random_value = float(np.random.normal(mean, std_dev))
          metadata.update({"mean": mean, "stdDev": std_dev})
          
      elif distribution_type == 3:  # Exponential distribution
          scale = distribution_parameters[0]
          random_value = float(np.random.exponential(scale))
          metadata.update({"scale": scale})
          
      elif distribution_type == 4:  # Binomial distribution
          n, p = distribution_parameters
          random_value = int(np.random.binomial(n, p))
          metadata.update({"trials": n, "probability": p})
          
      else:
          raise ValueError("Unsupported distribution type")
      
      result = {
          "requestId": request_id,
          "randomValue": random_value,
          "distributionMetadata": metadata
      }
      
      return result