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create the Stochastic Thinking MCP Server
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Why Stochastic Thinking Matters
When AI assistants make decisions - whether writing code, solving problems, or suggesting improvements - they often fall into patterns of "local thinking", similar to how we might get stuck trying the same approach repeatedly despite poor results. This is like being trapped in a valley when there's a better solution on the next mountain over, but you can't see it from where you are.
This server introduces advanced decision-making strategies that help break out of these local patterns:
Instead of just looking at the immediate next step (like basic Markov chains do), these algorithms can look multiple steps ahead and consider many possible futures
Rather than always picking the most obvious solution, they can strategically explore alternative approaches that might initially seem suboptimal
When faced with uncertainty, they can balance the need to exploit known good solutions with the potential benefit of exploring new ones
Think of it as giving your AI assistant a broader perspective - instead of just choosing the next best immediate action, it can now consider "What if I tried something completely different?" or "What might happen several steps down this path?"
A Model Context Protocol (MCP) server that provides stochastic algorithms and probabilistic decision-making capabilities, extending the sequential thinking server with advanced mathematical models.
Features
Stochastic Algorithms
Markov Decision Processes (MDPs)
Optimize policies over long sequences of decisions
Incorporate rewards and actions
Support for Q-learning and policy gradients
Configurable discount factors and state spaces
Monte Carlo Tree Search (MCTS)
Simulate future action sequences
Balance exploration and exploitation
Configurable simulation depth and exploration constants
Ideal for large decision spaces
Multi-Armed Bandit Models
Balance exploration vs exploitation
Support multiple strategies:
Epsilon-greedy
UCB (Upper Confidence Bound)
Thompson Sampling
Dynamic reward tracking
Bayesian Optimization
Optimize decisions with uncertainty
Probabilistic inference models
Configurable acquisition functions
Continuous parameter optimization
Hidden Markov Models (HMMs)
Infer latent states
Forward-backward algorithm
State sequence prediction
Emission probability modeling
Algorithm Selection Guide
Choose the appropriate algorithm based on your problem characteristics:
Markov Decision Processes (MDPs)
Best for:
Sequential decision-making problems
Problems with clear state transitions
Scenarios with defined rewards
Long-term optimization needs
Monte Carlo Tree Search (MCTS)
Best for:
Game playing and strategic planning
Large decision spaces
When simulation is possible
Real-time decision making
Multi-Armed Bandit
Best for:
A/B testing
Resource allocation
Online advertising
Quick adaptation needs
Bayesian Optimization
Best for:
Hyperparameter tuning
Expensive function optimization
Continuous parameter spaces
When uncertainty matters
Hidden Markov Models (HMMs)
Best for:
Time series analysis
Pattern recognition
State inference
Sequential data modeling
the code is as follows:
#!/usr/bin/env node
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { CallToolRequestSchema, ListToolsRequestSchema, McpError, ErrorCode, } from "@modelcontextprotocol/sdk/types.js";
// Stochastic Algorithm Implementations
class StochasticServer {
validateStochasticData(input) {
const data = input;
if (!data.algorithm || typeof data.algorithm !== 'string') {
throw new Error('Invalid algorithm: must be a string');
}
if (!data.problem || typeof data.problem !== 'string') {
throw new Error('Invalid problem: must be a string');
}
if (!data.parameters || typeof data.parameters !== 'object') {
throw new Error('Invalid parameters: must be an object');
}
return {
algorithm: data.algorithm,
problem: data.problem,
parameters: data.parameters,
result: typeof data.result === 'string' ? data.result : undefined
};
}
formatOutput(data) {
const { algorithm, problem, parameters, result } = data;
const border = '─'.repeat(Math.max(algorithm.length + 20, problem.length + 4));
let output = `
┌${border}┐
│ 🎲 Algorithm: ${algorithm.padEnd(border.length - 13)} │
├${border}┤
│ Problem: ${problem.padEnd(border.length - 10)} │
├${border}┤
│ Parameters:${' '.repeat(border.length - 12)} │`;
for (const [key, value] of Object.entries(parameters)) {
output += `\n│ • ${key}: ${String(value).padEnd(border.length - key.length - 4)} │`;
}
if (result) {
output += `\n├${border}┤
│ Result: ${result.padEnd(border.length - 9)} │`;
}
output += `\n└${border}┘`;
return output;
}
mdpOneLineSummary(params) {
return `Optimized policy over ${params.states || 'N'} states with discount factor ${params.gamma || 0.9}`;
}
mctsOneLineSummary(params) {
return `Explored ${params.simulations || 1000} paths with exploration constant ${params.explorationConstant || 1.4}`;
}
banditOneLineSummary(params) {
return `Selected optimal arm with ${params.strategy || 'epsilon-greedy'} strategy (ε=${params.epsilon || 0.1})`;
}
bayesianOneLineSummary(params) {
return `Optimized objective with ${params.acquisitionFunction || 'expected improvement'} acquisition`;
}
hmmOneLineSummary(params) {
return `Inferred hidden states using ${params.algorithm || 'forward-backward'} algorithm`;
}
processAlgorithm(input) {
try {
const validatedInput = this.validateStochasticData(input);
const formattedOutput = this.formatOutput(validatedInput);
console.error(formattedOutput);
let summary = '';
switch (validatedInput.algorithm) {
case 'mdp':
summary = this.mdpOneLineSummary(validatedInput.parameters);
break;
case 'mcts':
summary = this.mctsOneLineSummary(validatedInput.parameters);
break;
case 'bandit':
summary = this.banditOneLineSummary(validatedInput.parameters);
break;
case 'bayesian':
summary = this.bayesianOneLineSummary(validatedInput.parameters);
break;
case 'hmm':
summary = this.hmmOneLineSummary(validatedInput.parameters);
break;
}
return {
content: [{
type: "text",
text: JSON.stringify({
algorithm: validatedInput.algorithm,
status: 'success',
summary,
hasResult: !!validatedInput.result
}, null, 2)
}]
};
}
catch (error) {
return {
content: [{
type: "text",
text: JSON.stringify({
error: error instanceof Error ? error.message : String(error),
status: 'failed'
}, null, 2)
}],
isError: true
};
}
}
}
// Tool Definition
const STOCHASTIC_TOOL = {
name: "stochasticalgorithm",
description: `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.`,
inputSchema: {
type: "object",
properties: {
algorithm: {
type: "string",
enum: [
"mdp",
"mcts",
"bandit",
"bayesian",
"hmm"
]
},
problem: { type: "string" },
parameters: {
type: "object",
additionalProperties: true
},
result: { type: "string" }
},
required: ["algorithm", "problem", "parameters"]
}
};
// Server Instance
const stochasticServer = new StochasticServer();
const server = new Server({
name: "stochastic-thinking-server",
version: "0.1.0",
}, {
capabilities: {
tools: {
stochasticalgorithm: STOCHASTIC_TOOL
},
},
});
// Request Handlers
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [STOCHASTIC_TOOL],
}));
server.setRequestHandler(CallToolRequestSchema, async (request) => {
switch (request.params.name) {
case "stochasticalgorithm":
return stochasticServer.processAlgorithm(request.params.arguments);
default:
throw new McpError(ErrorCode.MethodNotFound, `Unknown tool: ${request.params.name}`);
}
});
async function runServer() {
const transport = new StdioServerTransport();
await server.connect(transport);
console.error("Stochastic Thinking MCP Server running on stdio");
}
runServer().catch((error) => {
console.error("Fatal error running server:", error);
process.exit(1);
});