{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-random-number","slug":"random-number","name":"Random Number","type":"mcp","url":"https://github.com/zazencodes/random-number-mcp","page_url":"https://unfragile.ai/random-number","categories":["mcp-servers"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-random-number__cap_0","uri":"capability://tool.use.integration.uniform.random.integer.generation","name":"uniform-random-integer-generation","description":"Generates 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.","intents":["Generate random test data or fixtures for unit tests","Create random IDs or tokens for prototyping","Simulate dice rolls or lottery draws in applications","Produce random samples for Monte Carlo simulations"],"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"],"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"],"requires":["Python 3.7+","MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM framework)","Network or stdio transport to MCP server"],"input_types":["integer (min boundary)","integer (max boundary)"],"output_types":["integer (random value within range)"],"categories":["tool-use-integration","automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-random-number__cap_1","uri":"capability://tool.use.integration.floating.point.random.generation","name":"floating-point-random-generation","description":"Generates 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.","intents":["Generate probabilities for decision-making in LLM agents","Create random weights or scaling factors for simulations","Produce continuous random values for Monte Carlo methods","Simulate stochastic processes or Markov chains"],"best_for":["LLM agents implementing probabilistic reasoning or sampling","Researchers prototyping stochastic simulations with LLM orchestration","Developers building games with randomized mechanics"],"limitations":["Fixed range [0.0, 1.0) — no parameterized range support","Floating-point precision limited to Python's float type (~15-17 significant digits)","Not cryptographically secure — unsuitable for security or gambling applications","No built-in statistical distribution support (e.g., Gaussian, exponential) — only uniform"],"requires":["Python 3.7+","MCP client with float number parsing support","Network or stdio transport to MCP server"],"input_types":[],"output_types":["float (random value in [0.0, 1.0))"],"categories":["tool-use-integration","automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-random-number__cap_2","uri":"capability://tool.use.integration.random.choice.from.sequence","name":"random-choice-from-sequence","description":"Selects 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.","intents":["Randomly select from a set of options or strategies in an LLM agent","Pick random test cases or scenarios from a predefined list","Sample random items from a catalog or inventory","Implement random branching in multi-path workflows"],"best_for":["LLM agents needing to randomly select from discrete options","Developers building randomized workflows or game logic","Teams prototyping A/B testing or multi-armed bandit scenarios"],"limitations":["Requires list to be non-empty — no error handling for empty sequences exposed","No weighted selection — all items have equal probability","List size limited by MCP message size constraints (typically 1MB+)","No sampling without replacement — each call is independent"],"requires":["Python 3.7+","MCP client supporting list/array serialization","Network or stdio transport to MCP server"],"input_types":["array/list (items to choose from)"],"output_types":["any (single element from input list)"],"categories":["tool-use-integration","automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-random-number__cap_3","uri":"capability://tool.use.integration.random.sequence.shuffling","name":"random-sequence-shuffling","description":"Randomly 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.","intents":["Randomize test case execution order for robustness testing","Shuffle a deck of cards or game items","Create random orderings for A/B test variants","Implement random sampling without replacement via shuffling"],"best_for":["QA engineers building randomized test suites with LLM orchestration","Game developers implementing card shuffling or randomized mechanics","Researchers prototyping randomized algorithms"],"limitations":["Returns a new list rather than modifying in-place — no reference semantics","Shuffle quality depends on Python's Mersenne Twister — not cryptographically secure","List size limited by MCP message size and memory constraints","No control over shuffle algorithm or randomness source"],"requires":["Python 3.7+","MCP client supporting list serialization and deserialization","Network or stdio transport to MCP server"],"input_types":["array/list (items to shuffle)"],"output_types":["array/list (shuffled copy of input)"],"categories":["tool-use-integration","automation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-random-number__cap_4","uri":"capability://tool.use.integration.mcp.protocol.tool.invocation","name":"mcp-protocol-tool-invocation","description":"Implements 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.","intents":["Enable Claude or other MCP clients to call random functions without plugins","Integrate random generation into LLM agent workflows via standard protocol","Expose random tools to custom MCP runners or frameworks","Build reproducible, auditable random generation chains in LLM applications"],"best_for":["Teams using Claude Desktop or Claude API with MCP support","Developers building custom MCP runners or LLM orchestration frameworks","Organizations standardizing on MCP for tool integration"],"limitations":["Requires MCP client support — not compatible with REST-only LLM APIs","Tool discovery happens at startup — dynamic tool registration not supported","No built-in authentication or rate limiting — relies on transport-level security","Error handling limited to JSON-RPC error responses — no custom exception types"],"requires":["Python 3.7+","MCP-compatible LLM client (Claude Desktop, Claude API with MCP, custom runner)","stdio or network transport configured between client and server"],"input_types":["JSON-RPC 2.0 request (tool name + parameters)"],"output_types":["JSON-RPC 2.0 response (tool result or error)"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-random-number__cap_5","uri":"capability://tool.use.integration.standard.library.only.random.backend","name":"standard-library-only-random-backend","description":"Uses 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.","intents":["Deploy random generation without managing external dependencies","Ensure compatibility across minimal Python environments","Avoid version conflicts with NumPy or other scientific libraries","Keep MCP server lightweight for embedded or resource-constrained deployments"],"best_for":["Teams with strict dependency management policies","Deployments in containerized or serverless environments","Projects avoiding scientific computing libraries"],"limitations":["No cryptographic randomness — unsuitable for security applications","Limited to uniform distributions — no Gaussian, exponential, or other statistical distributions","Mersenne Twister has known statistical weaknesses for certain use cases (e.g., low-order bits)","No parallel random number generation support"],"requires":["Python 3.7+ (no external packages)"],"input_types":[],"output_types":[],"categories":["tool-use-integration","automation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.7+","MCP client implementation (Claude Desktop, custom MCP runner, or compatible LLM framework)","Network or stdio transport to MCP server","MCP client with float number parsing support","MCP client supporting list/array serialization","MCP client supporting list serialization and deserialization","MCP-compatible LLM client (Claude Desktop, Claude API with MCP, custom runner)","stdio or network transport configured between client and server","Python 3.7+ (no external packages)"],"failure_modes":["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)","Not cryptographically secure — unsuitable for security or gambling applications","No built-in statistical distribution support (e.g., Gaussian, exponential) — only uniform","Requires list to be non-empty — no error handling for empty sequences exposed","No weighted selection — all items have equal probability","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.37,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=random-number","compare_url":"https://unfragile.ai/compare?artifact=random-number"}},"signature":"6QhlQM2XkSE877w0AD7xQmeh8om4BXbMsC6CuNmsPtD4qPAAmGEzQtmJeX7zdBFsHntZPYtmGfYJ+DSADhpIAw==","signedAt":"2026-06-22T18:32:05.934Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/random-number","artifact":"https://unfragile.ai/random-number","verify":"https://unfragile.ai/api/v1/verify?slug=random-number","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}