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Explorer/MCP/chirag127/stochastic-thinking-mcp-server
LOCAL

Stochastic Thinking Server

chirag127/stochastic-thinking-mcp-server

Provide advanced stochastic algorithms and probabilistic decision-making capabilities to enhance AI assistants' decision-making processes. Enable exploration of multiple future scenarios and strategic alternatives beyond immediate next steps. Improve AI's ability to balance exploration and exploitation in uncertain environments for better long-term outcomes.

1 tool available ↓
The Ghost
This server runs locally on your machine. We can't scan it remotely; review the source code before installing.
Time indexed (12mo)
1toolsLocal/ STDIO12moindexed
Checked Apr 28, 2026
Quality Score
23/95
Emerging
Risk Score
0/100
Clean
▸How is this calculated?
Quality Breakdown
Tenure16.1/20
364 days indexed
Capability6.5/25
Tools: 1.5/13 (1 tools)
Description: 5/5 ✓
Endpoint: 0/7 —
Adoption0/25
Use count: 0/20 (0 uses)
Multi-registry: 0/5 (1 registry)
Reliability0/25
Currently live: 0/10 —
Uptime history: 0/15 No checks yet
Security scan: 0 pts in v1.0; ready to weight when coverage improves
Incomplete Data Cap (60)
Usage data is not available for this server. Quality is capped until adoption can be measured.
Risk
0— Clean
No signals detected.
The scanner shows
This server runs locally. We can't scan it remotely; review the source code before installing.
First indexed May 18, 2025
Server Profile
⚙
Tools catalogued
1
1 tool available. Full list below.
☁
Hosting
Local / STDIO
Runs on your machine. Has access to your filesystem, SSH keys, and environment variables.
○
Registry presence
Not verified
Not yet verified by the Official MCP Registry.
●
Liveness
—
No liveness checks recorded yet.
Publisher Verification
Not yet verified by the Official MCP Registry.
Tools (1)
stochasticalgorithm
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.

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