🌳 Tree of Thoughts (ToT) MCP Server

Tree of Thoughts (ToT) is a powerful reasoning framework that enables AI models to explore multiple solution paths systematically. Think of it as a decision tree for thoughts—your AI can generate different approaches, evaluate them, backtrack when stuck, and focus on the most promising paths. Perfect for complex problem-solving, strategic planning, and multi-step reasoning tasks.

Whether you're solving puzzles, planning projects, or exploring creative alternatives, ToT provides structured exploration with evaluation scores, pruning strategies, and persistent storage for tracking reasoning over time.

✨ Features

• 🌲 Thought Trees - Create hierarchical thought structures with parent-child relationships
• 📊 Evaluation - Score thoughts to guide exploration toward promising paths
• ↩️ Backtracking - Mark thought branches as pruned and explore alternatives
• ✂️ Pruning - Automatically remove low-scoring branches
• 🏆 Best Path Selection - Identify and select the most promising thoughts
• 💾 Persistent Storage - Save and load thought trees across sessions with atomic writes and error handling
• 📈 Statistics - Track tree metrics (depth, evaluations, pruning rates)
• 🔍 Branching Strategies - Systematic exploration using BFS, DFS, beam search, and best-first search
• 🤖 LLM Integration - Optional LLM provider for automated thought generation with strict mode support
• 🛡️ Robust Traversal - Iterative implementations for handling very deep trees without recursion limits
• ✅ Schema Validation - Input validation for all tool parameters

🚀 Installation

npm install
npm run build

⚙️ Configuration

Add to your MCP client configuration (e.g., mcp.json):

{
  "mcpServers": {
    "tot": {
      "command": "node",
      "args": ["/path/to/ToT-mcp/dist/index.js"],
      "env": {
        "TOT_STORAGE_PATH": "/path/to/ToT-mcp/tot-storage.json",
        "TOT_OUTPUT_DIR": "/path/to/ToT-mcp/output"
      }
    }
  }
}

🎯 Quick Start

Basic Example

Create a tree to solve a problem:

{
  "goal": "Solve the 24 game with numbers [3, 8, 8, 8]",
  "rootContent": "Start with the numbers 3, 8, 8, 8",
  "maxDepth": 5
}

Add child thoughts with different approaches:

{
  "treeId": "tree-123",
  "parentId": "thought-456",
  "content": "Try multiplying 8 * 8 = 64, then 64 / 8 = 8, then 8 * 3 = 24"
}

Evaluate thoughts to guide exploration:

{
  "treeId": "tree-123",
  "thoughtId": "thought-789",
  "score": 0.95,
  "reasoning": "This approach successfully reaches the target of 24"
}

LLM Provider Configuration

The server supports optional LLM integration for automated thought generation. To configure an LLM provider, modify the server instantiation in src/index.ts:

const llmProvider = {
  generateThoughts: async (prompt: string, count: number, context?: string): Promise<string[]> => {
    // Your LLM implementation here
    return Array.from({ length: count }, (_, i) => `Generated thought ${i + 1}`);
  }
};

const config = {
  llmProvider,
  strictLLM: false // Set to true to throw errors when LLM is not configured
};

const server = new ToTMCPServer(config);

Strict Mode: When strictLLM is set to true, the server will throw an error if generate_children is called without an LLM provider configured. This prevents accidental use of placeholder thoughts in production environments.

Using with LLM Providers

The ToT service includes example LLM provider implementations in the examples/llm-providers/ directory:

Mock LLM Provider - A simple mock implementation for testing:

import { MockLLMProvider } from './examples/llm-providers/mock-llm-provider.js';

const llmProvider = new MockLLMProvider([
  'Consider exploring the most promising path first',
  'Try a different approach by breaking down the problem',
  'Evaluate the trade-offs between different solutions'
]);

const config = { llmProvider, strictLLM: false };
const service = new ToTService('./tot-storage.json', config);

Grok LLM Provider - Example implementation using xAI's Grok API (commented by default):

import { GrokLLMProvider } from './examples/llm-providers/grok-llm-provider.js';

const apiKey = process.env.GROK_API_KEY;
const llmProvider = new GrokLLMProvider(apiKey);

const config = { llmProvider, strictLLM: true };
const service = new ToTService('./tot-storage.json', config);

See examples/llm-providers/grok-llm-provider.ts for the full implementation. Remember to never hardcode API keys - use environment variables or secure configuration management.

Generate and Evaluate in One Step

The generateChildrenAndEvaluate method combines thought generation with automatic evaluation:

// Generate children with a default score of 50
const children = await service.generateChildrenAndEvaluate({
  treeId: 'tree-123',
  parentId: 'thought-456',
  numChildren: 3
}, 50);

// Generate children and use LLM as judge for evaluation
const childrenWithJudge = await service.generateChildrenAndEvaluate({
  treeId: 'tree-123',
  parentId: 'thought-456',
  numChildren: 3
}, undefined, true);

This method requires an LLM provider to be configured.

🛠️ Available Tools

Tree Management

create_tree

Create a new Tree of Thoughts with a root thought and goal.

Parameters:

• goal (string, required): The goal or problem this tree is solving
• rootContent (string, required): The content of the root thought
• maxDepth (number, optional): Maximum depth of the tree (default: 10)
• metadata (object, optional): Optional metadata for the tree

get_tree

Get a tree by ID.

Parameters:

• treeId (string, required): The ID of the tree to retrieve

list_trees

List all trees.

delete_tree

Delete a tree by ID.

Parameters:

• treeId (string, required): The ID of the tree to delete

Thought Operations

add_child

Add a child thought to an existing thought.

Parameters:

• treeId (string, required): The ID of the tree
• parentId (string, required): The ID of the parent thought
• content (string, required): The content of the child thought
• metadata (object, optional): Optional metadata for the thought

evaluate_thought

Evaluate a thought with a score.

Parameters:

• treeId (string, required): The ID of the tree
• thoughtId (string, required): The ID of the thought to evaluate
• score (number, required): The evaluation score (e.g., 0-1 or 0-100)
• reasoning (string, optional): Optional reasoning for the evaluation

select_thought

Mark a thought as selected for further exploration.

Parameters:

• treeId (string, required): The ID of the tree
• thoughtId (string, required): The ID of the thought to select

backtrack

Backtrack from a thought, marking all descendants as pruned.

Parameters:

• treeId (string, required): The ID of the tree
• thoughtId (string, required): The ID of the thought to backtrack from

prune_tree

Prune thoughts below a certain evaluation threshold.

Parameters:

• treeId (string, required): The ID of the tree
• threshold (number, required): The evaluation threshold (thoughts below this will be pruned)

Query Operations

get_thought

Get a specific thought by ID.

Parameters:

• treeId (string, required): The ID of the tree
• thoughtId (string, required): The ID of the thought to retrieve

get_tree_structure

Get the hierarchical structure of a tree.

Parameters:

• treeId (string, required): The ID of the tree

get_best_thoughts

Get the best evaluated thoughts in a tree.

Parameters:

• treeId (string, required): The ID of the tree
• limit (number, optional): Maximum number of thoughts to return (default: 5)

get_tree_stats

Get statistics about a tree.

Parameters:

• treeId (string, required): The ID of the tree

Returns:

• totalThoughts: Total number of thoughts in the tree
• evaluatedThoughts: Number of evaluated thoughts
• selectedThoughts: Number of selected thoughts
• prunedThoughts: Number of pruned thoughts
• maxDepthReached: Maximum depth reached in the tree
• averageEvaluation: Average evaluation score

System Operations

clear_all

Clear all trees.

save_state

Manually save the current state to storage.

get_version

Get the version information of this ToT MCP server.

explore_with_strategy

Explore a thought tree using a systematic branching strategy.

Parameters:

• treeId (string, required): The ID of the tree to explore
• strategy (string, required): The branching strategy to use (bfs, dfs, beam, or best_first)
• maxThoughts (number, optional): Maximum number of thoughts to explore (default: 100)
• beamWidth (number, optional): Beam width for beam search strategy (default: 3)
• stopCriteria (object, optional): Optional stop criteria
  • minEvaluation (number): Stop when a thought reaches this evaluation score
  • maxDepth (number): Stop when reaching this depth
  • targetThoughtCount (number): Stop when exploring this many thoughts

Returns:

• thoughtsExplored: Number of thoughts explored
• thoughtsCreated: Number of thoughts created during exploration
• maxDepthReached: Maximum depth reached
• bestThoughtId: ID of the best thought found
• bestEvaluation: Evaluation score of the best thought
• stoppedReason: Reason why exploration stopped

📖 Usage Example

Here's a typical workflow for solving a problem using ToT:

1. Create a tree with your goal and initial thought
2. Add child thoughts representing different approaches
3. Evaluate each thought based on its promise
4. Select the best thoughts for further exploration
5. Add more children to selected thoughts
6. Backtrack if a path doesn't work out
7. Prune low-scoring branches to focus resources
8. Review the tree structure to understand the reasoning path

📊 Data Structures

Thought

{
  id: string;
  content: string;
  parentId: string | null;
  children: string[];
  evaluation: number | null;
  state: 'pending' | 'evaluated' | 'selected' | 'pruned';
  depth: number;
  createdAt: string;
  metadata?: Record<string, any>;
}

Tree

{
  id: string;
  rootId: string;
  thoughts: Map<string, Thought>;
  goal: string;
  createdAt: string;
  updatedAt: string;
  maxDepth: number;
  metadata?: Record<string, any>;
}

💾 Storage

Thought trees are persisted to tot-storage.json in JSON format. The storage mechanism uses:

• Atomic Writes: Data is written to a temporary file first, then renamed to prevent corruption
• Error Handling: Graceful recovery from corrupt files with detailed error messages
• Schema Validation: Loaded data is validated to ensure structural integrity
• Graceful Degradation: Corrupt or missing files result in an empty state rather than crashes

Logs of tool calls are stored in the output directory with daily rotation.

📄 License

MIT