Stochastic Thinking Server

Name: Stochastic Thinking Server
Rating: 0.3 (6 reviews)
Author: chirag127

平台与服务

by chirag127

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.

6GitHub

什么是 Stochastic Thinking Server？

核心功能 (1 个工具)

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.

README

Stochastic Thinking MCP Server

Stochastic algorithms and probabilistic decision-making — as an MCP tool for AI agents.

What it is / why it exists

An MCP server that extends an agent's sequential thinking with probabilistic decision-making. When a problem involves uncertainty, sequential planning, or a large decision space, the agent calls one tool — stochasticalgorithm — and picks a mathematical model (MDP, MCTS, multi-armed bandit, Bayesian optimization, HMM) to escape local optima and explore alternative solution paths.

One tool, five algorithms, zero required configuration.

Architecture

mermaid

flowchart LR
    Client["MCP Client<br/>(Claude, Cursor, opencode, ...)"]
    subgraph Server["stochastic-thinking-mcp-server"]
        Tool["stochasticalgorithm"]
        Tool --> A1["MDP"]
        Tool --> A2["MCTS"]
        Tool --> A3["Bandit"]
        Tool --> A4["Bayesian"]
        Tool --> A5["HMM"]
    end
    Client -->|"Streamable HTTP (MCP 2.0) /mcp<br/>or stdio"| Server
    Server -->|"formatted decision summary"| Client

Algorithms

Algorithm	Use case
MDP — Markov Decision Processes	Sequential decisions with defined rewards
MCTS — Monte Carlo Tree Search	Game/strategy planning over large decision spaces
Multi-Armed Bandit	A/B testing, resource allocation, online learning
Bayesian Optimization	Hyperparameter tuning, expensive-function optimization
HMM — Hidden Markov Models	Time series, pattern recognition, state inference

Features

Single stochasticalgorithm tool with an algorithm selector: mdp · mcts · bandit · bayesian · hmm
Two transports: stdio (local clients) and Streamable HTTP (MCP 2.0, remote)
Zero required configuration — no keys, no external calls
Installable via npx, Smithery, or the hosted endpoint

Tech stack

Node.js (ESM, >=18)
@modelcontextprotocol/sdk ^1.29
esbuild bundle build, plain-JS test harness

Repo structure

code

Stochastic-Thinking-MCP-Server/
├── index.js          # tool logic + stdio transport
├── http.js           # Streamable HTTP transport (MCP 2.0) at /mcp
├── test.js           # test harness
├── docs/             # GitHub Pages landing (index.html + CNAME)
├── Dockerfile        # container image
├── smithery.yaml     # Smithery deploy config
└── package.json

Quick start

Install via Smithery

bash

npx -y @smithery/cli install @chirag127/stochastic-thinking-mcp-server --client claude

MCP client config

Hosted (Streamable HTTP):

json

{
  "mcpServers": {
    "stochastic-thinking": {
      "url": "https://stochastic-thinking-mcp-server.oriz.in/mcp"
    }
  }
}

Local (stdio):

json

{
  "mcpServers": {
    "stochastic-thinking": {
      "command": "node",
      "args": ["/path/to/Stochastic-Thinking-MCP-Server/index.js"]
    }
  }
}

Run it yourself

bash

npm install
npm start                         # stdio
HTTP_PORT=3778 node http.js       # Streamable HTTP at http://localhost:3778/mcp

Call the tool

json

{
  "algorithm": "mdp",
  "problem": "Optimize route selection for delivery vehicles",
  "parameters": { "states": 10, "gamma": 0.95, "learningRate": 0.1 }
}

Register it

MCP Registry: https://registry.modelcontextprotocol.io
Smithery: @chirag127/stochastic-thinking-mcp-server — https://smithery.ai/server/@chirag127/stochastic-thinking-mcp-server

Configuration

No configuration is required. Optional environment variables:

Variable	Purpose
`HTTP_PORT`	Port for the Streamable HTTP transport (default `3778`)

Part of the oriz family

One of ~80 sites and tools in the oriz family. Pairs with the Clear Thought MCP Server (structured reasoning) and knowledge-mcp (knowledge base).

Contributing

Issues and PRs welcome. Conventional commits are the changelog.

License

MIT — see LICENSE.

Author

Chirag Singhal · chirag@oriz.in · @chirag127

Status

Stable. Roadmap: more algorithms, richer parameter validation.

常见问题