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A Model Context Protocol (MCP) server that provides stochastic algorithms and probabilistic decision-making capabilities
Single tool with marginal definition quality. Tool name 'stochasticalgorithm' lacks action verb prefix (should be 'apply_stochastic_algorithm' or similar). Description is adequate length (194 chars) but generic and does not guide LLM on WHEN to use this tool vs alternatives. Input schema is present with proper JSON Schema structure, but parameter descriptions are minimal (e.g., 'Description of the problem to solve' provides no constraint guidance). The 'parameters' field uses additionalProperties:true, which is a code smell, it defeats validation and lets LLMs pass invalid algorithm-specific params without feedback. Output schema is not documented; LLMs cannot predict what fields to expect in responses. Error handling exists but only via try-catch with generic error messages in JSON, no recovery guidance (pattern:recovery-guide). Tool performs multiple distinct operations (MDP, MCTS, Bandit, Bayesian, HMM) under one umbrella, violating single-responsibility principle (pattern:tool). No tool annotations (readOnlyHint, destructiveHint, idempotentHint). No pagination or result limiting documented.
A tool for applying stochastic algorithms to decision-making problems. Supports various algorithms including: - Markov Decision Processes (MDPs): Optimize policies over long sequences of decisions - Monte Carlo Tree Search (MCTS): Simulate future action sequences for large decision spaces - Multi-Armed Bandit: Balance exploration vs exploitation in action selection - Bayesian Optimization: Optimize decisions with probabilistic inference - Hidden Markov Models (HMMs): Infer latent states affecting decision outcomes Each algorithm provides a systematic approach to handling uncertainty in decision-making.
Tool name lacks action verb. 'stochasticalgorithm' does not follow verb_noun naming convention. Should be 'apply_stochastic_algorithm' or 'optimize_with_stochastic_algorithm' to clarify intent.
Parameters field uses 'additionalProperties: true' instead of constraining valid keys. This defeats validation and invites hallucinated algorithm-specific params. LLMs cannot know which parameters are valid for each algorithm variant.
Parameter descriptions are under-specified. 'Description of the problem to solve' (problem param) gives no format constraints, example, or guidance. 'Algorithm-specific parameters' (parameters param) provides zero actionable detail.
Output schema is not documented. LLMs receive JSON with algorithm, status, summary, hasResult fields but have no formal schema definition to predict structure before calling.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 40 | <=2025-11-25 | v2 |
Single tool combines five distinct algorithms (MDP, MCTS, Bandit, Bayesian, HMM), violating single-responsibility principle. Each algorithm has different parameter requirements, outputs, and use cases. Should split into separate tools: apply_mdp, apply_mcts, apply_bandit, apply_bayesian, apply_hmm.
Error handling lacks recovery guidance. Catch block returns generic error JSON but does not tell LLM what to try next (pattern:recovery-guide). E.g., invalid algorithm should suggest valid options: 'Invalid algorithm: got "xyz", must be one of: mdp, mcts, bandit, bayesian, hmm'.
Tool description does not explain WHEN to use each algorithm variant. LLMs cannot select between MDP (long-horizon planning), MCTS (game tree search), Bandit (exploration-exploitation), Bayesian (probabilistic optimization), and HMM (latent state inference) without guidance on problem characteristics.
No tool annotations present (readOnlyHint, destructiveHint, idempotentHint). Tool performs simulations/optimizations with no side effects, should be marked readOnly. This helps agents understand retry safety.