Random Number
MCP ServerFree** - Provides LLMs with essential random generation abilities, built entirely on Python's standard library.
Capabilities6 decomposed
uniform-random-integer-generation
Medium confidenceGenerates uniformly distributed random integers within specified ranges using Python's random.randint() under the hood. The MCP server exposes this as a callable tool that LLMs can invoke with min/max parameters, abstracting away direct library calls and providing a standardized interface for deterministic or seeded random generation across multiple LLM provider integrations.
Exposes Python's standard library random.randint() as an MCP-compatible tool, allowing LLMs to request random integers without direct library imports or external API calls, leveraging the MCP protocol for standardized tool invocation across multiple LLM providers.
Simpler and more lightweight than external random APIs (like random.org) because it runs locally on the MCP server without network latency or rate limits, though sacrifices cryptographic quality for speed.
floating-point-random-generation
Medium confidenceGenerates uniformly distributed random floating-point numbers in the range [0.0, 1.0) using Python's random.random() function, exposed via MCP tool interface. The server handles the conversion and formatting of float outputs for LLM consumption, enabling probabilistic logic, weighted sampling, and continuous-value simulations without requiring external libraries.
Provides parameterless float generation via MCP, allowing LLMs to request random probabilities without configuration, using Python's built-in random.random() for minimal overhead and maximum portability across MCP implementations.
More efficient than calling external random APIs for each probability value because it executes locally with zero network latency, though less flexible than libraries like NumPy that support arbitrary distributions.
random-choice-from-sequence
Medium confidenceSelects a random element from a provided list or sequence using Python's random.choice(), exposed as an MCP tool that accepts a list of items and returns one uniformly at random. The server handles list serialization/deserialization and ensures type safety for heterogeneous collections, enabling LLMs to make random selections without implementing choice logic themselves.
Wraps Python's random.choice() as an MCP tool, allowing LLMs to request random selections from arbitrary lists without implementing choice logic, with support for heterogeneous item types (strings, numbers, objects) via JSON serialization.
More flexible than hardcoded random integer generation because it works with semantic item lists (e.g., strategy names, URLs) rather than numeric indices, though less powerful than weighted sampling libraries like NumPy.
random-sequence-shuffling
Medium confidenceRandomly reorders elements in a provided list using Python's random.shuffle() function, exposed via MCP as a tool that accepts a sequence and returns a shuffled copy. The server handles in-place shuffling internally and returns the permuted list to the LLM, enabling randomized orderings for testing, sampling, and stochastic algorithms without external dependencies.
Exposes Python's random.shuffle() as an MCP tool, allowing LLMs to request randomized orderings of lists without implementing Fisher-Yates or other shuffle algorithms, with support for any list type via JSON serialization.
Simpler than implementing shuffle logic in LLM prompts because it delegates to a proven standard library function, though less flexible than libraries offering multiple shuffle algorithms or seeded reproducibility.
mcp-protocol-tool-invocation
Medium confidenceImplements the Model Context Protocol (MCP) server interface, allowing Claude and other MCP-compatible LLMs to discover and invoke random generation tools via standardized JSON-RPC calls. The server exposes tool schemas (name, description, input parameters) that LLMs parse to understand capabilities, then routes tool calls back to Python random functions with parameter validation and error handling.
Implements the MCP server specification, exposing random tools via standardized JSON-RPC protocol with automatic tool schema generation, allowing LLMs to discover and invoke capabilities without hardcoding or custom bindings.
More portable than custom plugin systems because MCP is a standard protocol supported by multiple LLM providers, though requires MCP client support which not all LLM APIs provide yet.
standard-library-only-random-backend
Medium confidenceUses Python's built-in random module (Mersenne Twister PRNG) as the sole randomness source, with no external dependencies like NumPy or cryptography libraries. This design choice minimizes deployment footprint and ensures compatibility across Python environments, while exposing all standard library random functions (randint, random, choice, shuffle) through the MCP interface.
Deliberately constrains implementation to Python's standard library random module, avoiding external dependencies entirely and ensuring minimal deployment footprint and maximum environment compatibility.
Lighter and more portable than NumPy-based solutions because it requires zero external packages, though sacrifices statistical quality and performance for large-scale simulations.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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random-generator
Generate cryptographically secure integers, floats, bytes, UUIDs, strings, booleans, and list selections. Ensure unpredictable, unbiased results for security-sensitive workflows, simulations, testing, and fair draws. Save time by accessing high-quality randomness without custom implementations.
drand
Fetch publicly verifiable randomness from the drand quicknet. Retrieve the latest value or look up randomness by round or timestamp. Seed simulations, raffles, and cryptographic workflows with unbiased entropy.
KST Time & Randomizer
Get the current date and time in Korea Standard Time. Draw a random number between 1 and 50 for quick picks, testing, or games. Streamline tasks that need precise local time and simple randomness.
Python Snippets 3
New auto suggestion for Python updated in 2024
JAX
Google's numerical computing library — autodiff, JIT, vectorization, NumPy API for ML research.
Random Recall
Generate random numbers and recall the last one to test stateful workflows. Accelerate demos and integration tests with simple randomness that persists between calls. Tailor behavior with basic configuration to fit your needs.
Best For
- ✓LLM agents building test suites or synthetic datasets
- ✓Developers prototyping games or simulations with LLM logic
- ✓Teams using Claude or other MCP-compatible models for automation
- ✓LLM agents implementing probabilistic reasoning or sampling
- ✓Researchers prototyping stochastic simulations with LLM orchestration
- ✓Developers building games with randomized mechanics
- ✓LLM agents needing to randomly select from discrete options
- ✓Developers building randomized workflows or game logic
Known Limitations
- ⚠Limited to integer ranges only — no floating-point support
- ⚠No cryptographic randomness — uses Python's Mersenne Twister, unsuitable for security-sensitive applications
- ⚠Single-threaded execution — concurrent requests may serialize
- ⚠No built-in seeding control exposed via MCP interface — reproducibility requires external state management
- ⚠Fixed range [0.0, 1.0) — no parameterized range support
- ⚠Floating-point precision limited to Python's float type (~15-17 significant digits)
Requirements
Input / Output
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** - Provides LLMs with essential random generation abilities, built entirely on Python's standard library.
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