stochasticalgorithm
Apply stochastic algorithms like MDPs, MCTS, and Bayesian Optimization to optimize decision-making under uncertainty for complex problems.
Instructions
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.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| algorithm | Yes | ||
| problem | Yes | ||
| parameters | Yes | ||
| result | No |
Implementation Reference
- index.js:73-119 (handler)Core handler function that executes the stochasticalgorithm tool logic: validates input, logs formatted output, generates algorithm-specific one-line summary, and returns structured JSON response.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 }; } }
- index.js:123-156 (schema)Tool schema defining name, description, and input validation schema for stochasticalgorithm.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"] } };
- index.js:159-169 (registration)Server initialization and registration of the stochasticalgorithm tool in capabilities.const stochasticServer = new StochasticServer(); const server = new Server({ name: "stochastic-thinking-server", version: "0.1.0", }, { capabilities: { tools: { stochasticalgorithm: STOCHASTIC_TOOL }, }, });
- index.js:176-183 (registration)Request handler registration for CallToolRequestSchema, dispatching stochasticalgorithm calls to the handler.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}`); } });
- index.js:13-30 (helper)Helper function to validate and normalize input data for the stochastic algorithm tool.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 }; }