Stochastic Thinking
A stochastic algorithms MCP server built by Chirag Singhal that provides probabilistic decision-making capabilities to help AI assistants break out of local thinking patterns. The server implements five core algorithms - Markov Decision Processes for sequential optimization, Monte Carlo Tree Search for strategic planning, Multi-Armed Bandit models for exploration-exploitation balance, Bayesian Optimization for uncertainty-aware decisions, and Hidden Markov Models for state inference. Rather than always choosing the most obvious solution, it enables AI to strategically explore alternative approaches and consider multiple future scenarios, making it useful for game playing, A/B testing, hyperparameter tuning, route optimization, and any decision-making task where breaking out of deterministic patterns could yield better outcomes.
Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.
View full trust & usage report →Metadata
- Version
- 1.0.0
- Skill type
- atomic
- Execution layer
- mcp-remote
- Category
- ai-ml
- Source
- PulseMCP
- Author type
- human
- Updated
- 2026-04-25
Use via MCP
Resolve Stochastic Thinking from your agent
Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.
One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp--scope user for --scope project to commit it to .mcp.json.