generate_random_weighted
Select options randomly based on predefined weights for use in lottery systems, weighted item drops, task assignment, or A/B testing with customizable probability distributions.
Instructions
Weighted Random Selector
Randomly select an option based on weights
Args:
options (List[str]): List of options
weights (List[int]): Corresponding weight list (0-1000)
salt (str, optional): Random number salt value. Defaults to "".
Returns:
str: JSON string containing the selection result
Application Scenarios:
1. Lottery systems (prizes with different probabilities)
2. Random drops (weighted item drops)
3. Task assignment (based on priority)
4. A/B testing (experiment groups with different ratios)Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| options | Yes | ||
| salt | No | ||
| weights | Yes |
Implementation Reference
- main.py:92-112 (handler)The handler function for the MCP tool 'generate_random_weighted', decorated with @mcp.tool() for registration. It receives input parameters and delegates execution to the weighted_random_selector helper in utils.py, returning the result as a string.
@mcp.tool() async def generate_random_weighted(options: List[str], weights: List[int], salt: str = "") -> str: """Weighted Random Selector Randomly select an option based on weights Args: options (List[str]): List of options weights (List[int]): Corresponding weight list (0-1000) salt (str, optional): Random number salt value. Defaults to "". Returns: str: JSON string containing the selection result Application Scenarios: 1. Lottery systems (prizes with different probabilities) 2. Random drops (weighted item drops) 3. Task assignment (based on priority) 4. A/B testing (experiment groups with different ratios) """ return await weighted_random_selector(options, weights, salt) - utils.py:268-306 (helper)The core helper function implementing the weighted random selection logic. It derives a seed from blockchain block hashes and salt, normalizes weights, and uses numpy.random.choice to select an option probabilistically.
async def weighted_random_selector(options: List[str], weights: List[int], salt: str = "") -> Dict: """ Weighted random selector Randomly select an option based on weights Args: options: List of options to choose from weights: List of weights for each option (0-1000) salt: Optional salt value for additional randomness Returns: Dict containing selected option and selection metadata """ if len(options) != len(weights): raise ValueError("Options and weights must have the same length") 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) # Normalize weights weights_array = np.array(weights, dtype=float) weights_normalized = weights_array / np.sum(weights_array) # Select based on weights selection_index = np.random.choice(len(options), p=weights_normalized) selected_option = options[selection_index] result = { "requestId": request_id, "selectedOption": selected_option, "selectionIndex": int(selection_index) } return result