The Stochastic Thinking MCP Server provides stochastic algorithms and probabilistic decision-making tools to help AI agents escape local optima and explore alternative solution paths, enabling systematic handling of uncertainty in decision-making. It offers a suite of algorithms accessible through a single unified tool, stochasticalgorithm, which requires specifying the algorithm, a problem description, and algorithm-specific parameters, and returns a string result containing the solution.
Markov Decision Processes (MDP): Optimize policies over long sequences of decisions with clearly defined rewards (e.g., route optimization, resource planning).
Monte Carlo Tree Search (MCTS): Simulate future action sequences for strategic planning in games and large decision spaces.
Multi-Armed Bandit: Balance exploration vs. exploitation in action selection, applicable to A/B testing, resource allocation, and online learning.
Bayesian Optimization: Optimize decisions using probabilistic inference, ideal for hyperparameter tuning and expensive function optimization.
Hidden Markov Models (HMM): Infer latent states affecting decision outcomes, useful for time series analysis and pattern recognition.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Stochastic Thinking MCP Server use MCTS to plan the best chess opening moves for white"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Stochastic algorithms and probabilistic decision-making — as an MCP tool for AI agents.
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.
Related MCP server: Optimization MCP
Live MCP endpoint: https://stochastic-thinking-mcp-server.oriz.in (Streamable HTTP, MCP 2.0)
GitHub Pages: https://chirag127.github.io/Stochastic-Thinking-MCP-Server/
Smithery: https://smithery.ai/server/@chirag127/stochastic-thinking-mcp-server
Repo: https://github.com/chirag127/Stochastic-Thinking-MCP-Server
⭐ If this is useful, please star the repo — it helps others find it.
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"| ClientAlgorithm | 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 |
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
Node.js (ESM, >=18)
esbuild bundle build, plain-JS test harness
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.jsonnpx -y @smithery/cli install @chirag127/stochastic-thinking-mcp-server --client claudeHosted (Streamable HTTP):
{
"mcpServers": {
"stochastic-thinking": {
"url": "https://stochastic-thinking-mcp-server.oriz.in/mcp"
}
}
}Local (stdio):
{
"mcpServers": {
"stochastic-thinking": {
"command": "node",
"args": ["/path/to/Stochastic-Thinking-MCP-Server/index.js"]
}
}
}npm install
npm start # stdio
HTTP_PORT=3778 node http.js # Streamable HTTP at http://localhost:3778/mcp{
"algorithm": "mdp",
"problem": "Optimize route selection for delivery vehicles",
"parameters": { "states": 10, "gamma": 0.95, "learningRate": 0.1 }
}MCP Registry: https://registry.modelcontextprotocol.io
Smithery: @chirag127/stochastic-thinking-mcp-server — https://smithery.ai/server/@chirag127/stochastic-thinking-mcp-server
No configuration is required. Optional environment variables:
Variable | Purpose |
| Port for the Streamable HTTP transport (default |
One of ~80 sites and tools in the oriz family. Pairs with the Clear Thought MCP Server (structured reasoning) and knowledge-mcp (knowledge base).
Issues and PRs welcome. Conventional commits are the changelog.
MIT — see LICENSE.
Chirag Singhal · chirag@oriz.in · @chirag127
Stable. Roadmap: more algorithms, richer parameter validation.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| algorithm | Yes | ||
| problem | Yes | ||
| parameters | Yes | ||
| result | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'systematic approach to handling uncertainty' but lacks critical behavioral details: whether this is a read/write operation, computational requirements, error handling, or output format. For a complex tool with 4 parameters, this is insufficient disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a clear opening sentence followed by a bulleted list of algorithms and a summary. Each sentence adds value by explaining algorithm purposes. It could be slightly more concise by integrating the list more tightly, but it's generally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (4 parameters, nested objects, no output schema, and 0% schema coverage), the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or detailed parameter usage. For a stochastic algorithm tool with significant input complexity, this leaves too many gaps for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lists algorithm names (mapping to the 'algorithm' enum) and implies 'problem' is a decision-making issue, but doesn't explain 'parameters' object structure or 'result' parameter. This adds some meaning but doesn't fully cover the 4 parameters, especially the complex 'parameters' object.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: applying stochastic algorithms to decision-making problems. It lists specific algorithms (MDPs, MCTS, etc.) and explains they handle uncertainty, which is more specific than just restating the name. However, without sibling tools, it cannot demonstrate differentiation from alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus other approaches. It lists algorithm types but doesn't specify scenarios, prerequisites, or exclusions for usage. There are no sibling tools mentioned, but general context for application is missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
stochasticalgorithmScored across 1 tool
With only one tool, there is no ambiguity or overlap between tools. The tool 'stochasticalgorithm' clearly encompasses all stochastic algorithm applications for decision-making, leaving no room for confusion or misselection.
A single tool name follows a consistent pattern by using a descriptive compound term 'stochasticalgorithm'. There are no other tools to compare against, so naming consistency is inherently perfect with no deviations or mixed conventions.
The server has only one tool, which feels too thin for the broad scope implied by covering multiple stochastic algorithms like MDPs, MCTS, and Bayesian Optimization. A single tool handling such diverse algorithms may lack granularity and could be better served by multiple specialized tools for clearer functionality.
The tool covers various stochastic algorithms, but as a single tool, it may lack completeness in terms of specific operations like configuring, running, or analyzing results for each algorithm separately. There are no obvious gaps in the algorithms listed, but the surface might be too monolithic for effective agent use without more detailed tool breakdowns.
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