UltraThink
# UltraThink MCP Server
<div align="center">
**A Python MCP server for sequential thinking and problem-solving**
[](https://www.python.org/downloads/)
[](https://github.com/jlowin/fastmcp)
[](LICENSE)
[](https://github.com/husniadil/ekstend/tree/main/plugins/ultrathink)
</div>
---
> **Enhanced Python port** of the [Sequential Thinking MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking) by Anthropic.
> Maintains full compatibility while adding **confidence scoring**, **auto-assigned thought numbers**, and **multi-session support**.
> [!TIP]
> **Using Claude Code?** Install the [UltraThink Plugin](https://github.com/husniadil/ekstend/tree/main/plugins/ultrathink) for seamless integration - no MCP server setup required!
> ```bash
> # Via terminal
> claude plugin marketplace add husniadil/ekstend
> claude plugin install ultrathink@ekstend
>
> # Or interactively in Claude Code
> /plugin marketplace add husniadil/ekstend
> /plugin install ultrathink@ekstend
> ```
> [!NOTE]
> **Meta**: This MCP server was built iteratively using UltraThink itself - a practical example of the tool's capability to break down complex problems, manage architectural decisions, and maintain context across development sessions.
---
## Features
- **UltraThink**: Break down complex problems into manageable steps
- **Dynamic Adjustments**: Revise and refine thoughts as understanding deepens
- **Branching**: Explore alternative paths of reasoning
- **Confidence Scoring**: Explicit uncertainty tracking (0.0-1.0 scale)
- **Auto-adjustment**: Automatically adjusts total thoughts if needed
- **Multi-Session Support**: Manage multiple concurrent thinking sessions with session IDs
- **Formatted Logging**: Colored terminal output with rich formatting (can be disabled)
- **100% Test Coverage**: Comprehensive test suite with full code coverage
- **Type Safety**: Full mypy strict mode type checking for production code
- **Simple Layered Architecture**: Clean separation with models, services, and interface layers
## Installation
### Quick Install (Recommended)
Run directly with uvx from GitHub (no installation needed):
```bash
uvx --from git+https://github.com/husniadil/ultrathink ultrathink
```
### Development Setup
For local development:
```bash
# Clone the repository
git clone https://github.com/husniadil/ultrathink.git
cd ultrathink
# Install all dependencies (including dev dependencies)
uv sync
```
## Usage
### Task Commands (npm-like)
```bash
# List all available tasks
uv run task --list
# Run the server
uv run task run
# Run tests with coverage
uv run task test
# Run tests without coverage (quick)
uv run task test-quick
# Run the test client
uv run task client
# Format code (ruff + prettier)
uv run task format
# Lint code
uv run task lint
# Type check with mypy
uv run task typecheck
# Clean cache files
uv run task clean
```
### Direct Commands (Alternative)
For direct execution without task runner:
```bash
# Run the server directly
uv run ultrathink
# Run the test client directly
uv run python examples/client.py
```
**Note:** For testing, linting, and formatting, prefer using `uv run task` commands shown above.
## Tool: ultrathink
The server provides a single tool for dynamic and reflective problem-solving through structured thinking.
### Parameters
**Required:**
- `thought` (str): Your current thinking step
- `total_thoughts` (int): Estimated total thoughts needed (>=1)
**Optional:**
- `thought_number` (int): Current thought number - auto-assigned sequentially if omitted (1, 2, 3...), or provide explicit number for branching/semantic control
- `next_thought_needed` (bool): Whether another thought step is needed. Auto-assigned as `thought_number < total_thoughts` if omitted. Set explicitly to override default behavior
- `session_id` (str): Session identifier for managing multiple thinking sessions (None = create new, provide ID to continue session)
- `is_revision` (bool): Whether this revises previous thinking
- `revises_thought` (int): Which thought number is being reconsidered
- `branch_from_thought` (int): Branching point thought number
- `branch_id` (str): Branch identifier
- `needs_more_thoughts` (bool): If more thoughts are needed
- `confidence` (float): Confidence level (0.0-1.0, e.g., 0.7 for 70% confident)
- `uncertainty_notes` (str): Optional explanation for doubts or concerns about this thought
- `outcome` (str): What was achieved or expected as result of this thought
- `assumptions` (list[Assumption]): Assumptions made in this thought (id, text, confidence, critical, verifiable)
- `depends_on_assumptions` (list[str]): Assumption IDs this thought depends on (e.g., ["A1", "A2"])
- `invalidates_assumptions` (list[str]): Assumption IDs proven false (e.g., ["A3"])
### Response
Returns a JSON object with:
- `session_id`: Session identifier for continuation
- `thought_number`: Current thought number
- `total_thoughts`: Total thoughts (auto-adjusted if needed)
- `next_thought_needed`: Whether more thinking is needed
- `branches`: List of branch IDs
- `thought_history_length`: Number of thoughts processed in this session
- `confidence`: Confidence level of this thought (0.0-1.0, optional)
- `uncertainty_notes`: Explanation for doubts or concerns (optional)
- `outcome`: What was achieved or expected (optional)
- `all_assumptions`: All assumptions tracked in this session (keyed by ID)
- `risky_assumptions`: IDs of risky assumptions (critical + low confidence + unverified)
- `falsified_assumptions`: IDs of assumptions proven false
### Example
#### Basic Usage
```python
from fastmcp import Client
from ultrathink import mcp
async with Client(mcp) as client:
# Simple sequential thinking with auto-assigned fields
result = await client.call_tool("ultrathink", {
"thought": "Let me analyze this problem step by step",
"total_thoughts": 3
# thought_number auto-assigned: 1
# next_thought_needed auto-assigned: True (1 < 3)
})
```
#### With Enhanced Features
```python
async with Client(mcp) as client:
# With confidence scoring and explicit session
result = await client.call_tool("ultrathink", {
"thought": "Initial hypothesis - this approach might work",
"total_thoughts": 5,
"confidence": 0.6, # 60% confident
# next_thought_needed auto-assigned: True
"session_id": "problem-solving-session-1"
})
# Continue the same session with higher confidence
result2 = await client.call_tool("ultrathink", {
"thought": "After analysis, I'm more certain about this solution",
"total_thoughts": 5,
"confidence": 0.9, # 90% confident
# next_thought_needed auto-assigned: True
"session_id": "problem-solving-session-1" # Same session
})
# Branch from a previous thought
result3 = await client.call_tool("ultrathink", {
"thought": "Let me explore an alternative approach",
"total_thoughts": 6,
"confidence": 0.7,
"branch_from_thought": 1,
"branch_id": "alternative-path",
# next_thought_needed auto-assigned: True
"session_id": "problem-solving-session-1"
})
```
#### With Uncertainty Notes and Outcome
```python
async with Client(mcp) as client:
# Track uncertainty and outcomes
result = await client.call_tool("ultrathink", {
"thought": "Testing the authentication fix",
"total_thoughts": 5,
"confidence": 0.8,
"uncertainty_notes": "Haven't tested under high load yet",
"outcome": "Login flow works for standard users"
})
# Response includes the new fields
print(result["confidence"]) # 0.8
print(result["uncertainty_notes"]) # "Haven't tested under high load yet"
print(result["outcome"]) # "Login flow works for standard users"
```
#### With Assumption Tracking
```python
async with Client(mcp) as client:
# Thought 1: State assumptions explicitly
result = await client.call_tool("ultrathink", {
"thought": "Redis should meet our performance requirements",
"total_thoughts": 4,
"assumptions": [
{
"id": "A1",
"text": "Network latency to Redis < 5ms",
"confidence": 0.8,
"critical": True,
"verifiable": True,
"evidence": "Based on preliminary network tests in staging environment"
}
]
})
# Thought 2: Build on previous assumptions
result2 = await client.call_tool("ultrathink", {
"thought": "Based on low latency, Redis can handle 10K req/sec",
"total_thoughts": 4,
"depends_on_assumptions": ["A1"],
"session_id": result["session_id"]
})
# Thought 3: Invalidate if proven false
result3 = await client.call_tool("ultrathink", {
"thought": "After testing, latency is 15ms, not 5ms!",
"total_thoughts": 4,
"invalidates_assumptions": ["A1"],
"session_id": result["session_id"]
})
# Track all assumptions and detect risky ones
print(result3["all_assumptions"]) # {"A1": {...}}
print(result3["falsified_assumptions"]) # ["A1"]
```
## Configuration
### Environment Variables
- `DISABLE_THOUGHT_LOGGING`: Set to `"true"` to disable colored thought logging to stderr
### Usage with Claude Desktop
Add to your `claude_desktop_config.json`:
#### Using uvx from GitHub (Recommended)
```json
{
"mcpServers": {
"UltraThink": {
"command": "uvx",
"args": [
"--from",
"git+https://github.com/husniadil/ultrathink",
"ultrathink"
]
}
}
}
```
#### Local Development
For local development from source:
```json
{
"mcpServers": {
"UltraThink": {
"command": "uv",
"args": ["--directory", "/path/to/ultrathink", "run", "ultrathink"]
}
}
}
```
### Local Configuration File
For local development and testing, you can create a `.mcp.json` file (see `.mcp.json.example`):
```bash
# Copy the example file
cp .mcp.json.example .mcp.json
# Edit to match your local path
# Change /path/to/ultrathink to your actual directory
```
Example configuration (`.mcp.json.example`):
```json
{
"mcpServers": {
"UltraThink": {
"command": "uv",
"args": ["--directory", "/path/to/ultrathink", "run", "ultrathink"],
"env": {
"DISABLE_THOUGHT_LOGGING": "false"
}
}
}
}
```
This configuration:
- Enables thought logging by default (`DISABLE_THOUGHT_LOGGING: "false"`)
- Can be used with MCP clients that support `.mcp.json` configuration
- Useful for testing the server locally with colored output enabled
- **Note:** `.mcp.json` is gitignored - customize it for your local setup
## Session Management
### Session Lifecycle
**Important:** Sessions are stored **in-memory only** and will be lost when the server restarts or terminates. Each session is identified by a unique session ID and maintains:
- Thought history for that session
- Branch tracking
- Sequential thought numbering
**Implications:**
- Sessions do not persist across server restarts
- All thinking context is lost when the server stops
- For production use cases requiring persistent sessions, you would need to implement custom session persistence (e.g., to disk, database, or external state management)
**Best Practices:**
- Use custom session IDs (instead of auto-generated UUIDs) for resilient recovery if you need to recreate session context
- Keep session-critical information in your application layer if persistence is required
- Consider sessions as ephemeral working memory for active problem-solving tasks
## Architecture
Built with **Simple Layered Architecture** principles for clean separation of concerns and maintainable code.
### Files
**src/ultrathink/** (3-layer structure)
**Models Layer** (`models/`)
- **thought.py**: Thought, ThoughtRequest, ThoughtResponse models
- **session.py**: ThinkingSession model
**Services Layer** (`services/`)
- **thinking_service.py**: UltraThinkService business logic
**Interface Layer** (`interface/`)
- **mcp_server.py**: MCP server entry point with FastMCP tool registration
**Package Entry Points**
- **\_\_init\_\_.py**: Package exports
- **\_\_main\_\_.py**: CLI entry point (enables `uv run ultrathink`)
**tests/** (100% coverage, mirroring source structure)
**Models Tests** (`models/`)
- **test_thought.py**: Thought model tests (properties, formatting)
- **test_session.py**: Session logging and formatting tests
**Services Tests** (`services/`)
- **test_thinking_service.py**: Service tests (validation, functionality, branching, multi-session)
**Interface Tests** (`interface/`)
- **test_mcp_server.py**: MCP tool function tests
**Root Test Files**
- **test_cli.py**: CLI entry point tests
**examples/**
- **client.py**: Test client demonstrating tool usage
### Architecture Layers
#### 1. Models Layer
Pydantic models for data representation and validation:
**Thought**: Core model representing a single thought with validation and behaviors
**ThoughtRequest**: Input model from MCP clients with validation
**ThoughtResponse**: Output model to MCP clients with structured data
**ThinkingSession**: Session model managing thought history and branches
```python
# Type-safe model usage
request = ThoughtRequest(
thought="My thinking step",
thought_number=1,
total_thoughts=3,
next_thought_needed=True
)
response = ThoughtResponse(
thought_number=1,
total_thoughts=3,
next_thought_needed=True,
branches=[],
thought_history_length=1
)
```
#### 2. Services Layer
Business logic and orchestration:
**UltraThinkService**: Orchestrates the thinking process
**Responsibilities:**
- **Model Translation**: `ThoughtRequest → Thought` model (input)
- **Business Logic**: Delegate to `ThinkingSession`
- **Response Building**: Session state → `ThoughtResponse` model (output)
- **Validation**: Leverages Pydantic for automatic validation
- **Session Management**: Create and manage multiple thinking sessions
**Key Method:**
- `process_thought(request: ThoughtRequest) → ThoughtResponse`: Main orchestration
```python
service = UltraThinkService()
# Full flow:
# 1. Receives ThoughtRequest from interface layer
# 2. Translates to Thought model
# 3. Calls session.add_thought() (business logic)
# 4. Builds ThoughtResponse from session state
# 5. Returns response
request = ThoughtRequest(thought="...", thought_number=1, ...)
response = service.process_thought(request)
```
#### 3. Interface Layer
External interface using FastMCP:
**mcp_server.py**: MCP server tool registration
**Responsibilities:**
- Define MCP tools using `@mcp.tool` decorator
- Map tool parameters to model types
- Call services layer for processing
- Return responses to MCP clients
```python
@mcp.tool
def ultrathink(thought: str, total_thoughts: int, ...) -> ThoughtResponse:
request = ThoughtRequest(thought=thought, total_thoughts=total_thoughts, ...)
return thinking_service.process_thought(request)
```
**Type Safety Benefits:**
- Pydantic validation on all inputs/outputs
- No arbitrary dicts - strict typing throughout
- Automatic validation errors
- Clear separation between interface and business logic
### Architecture Benefits
1. **Clear Separation of Concerns**:
- Models layer = Data models with validation and behaviors
- Services layer = Business logic and orchestration
- Interface layer = External API (MCP tools)
2. **Simpler Structure**: Flatter folder hierarchy (2 levels instead of 3)
3. **Easier Imports**: Shorter relative import paths (`..models` vs `...domain.entities`)
4. **Consolidated Models**: Related models grouped together (Thought, ThoughtRequest, ThoughtResponse in one file)
5. **Testable**: Easy to test each layer in isolation
6. **Maintainable**:
- Change interface? → Update interface layer only
- Change business rules? → Update services layer only
- Change validation? → Update models layer only
7. **Extensible**: Easy to add new models, services, or tools
8. **Interface Independence**: Services can be reused with different interfaces (REST API, gRPC, CLI, etc.)
9. **Type Safety**: Pydantic models throughout ensure validation at all boundaries
## Development
### Running Tests
```bash
# Run all tests with coverage (recommended)
uv run task test
# Run tests without coverage (quick)
uv run task test-quick
# Coverage is 100%
```
### Type Checking
```bash
# Run mypy type checker on all code
uv run task typecheck
# Mypy runs in strict mode on entire codebase
```
The project uses **mypy in strict mode** across the entire codebase (`src/`, `tests/`, `examples/`) to ensure complete type safety.
### Test Organization
Tests are organized by layers, mirroring the source structure (100% coverage):
**Models Layer Tests** (`tests/models/`)
- **test_thought.py**: Model properties, auto-adjustment, formatting, validation, and confidence scoring
- **test_session.py**: Session logging and formatted output
**Services Layer Tests** (`tests/services/`)
- **test_thinking_service.py**: Service validation, functionality, branching, edge cases, response format, reference validation, and multi-session support
**Interface Layer Tests** (`tests/interface/`)
- **test_mcp_server.py**: MCP tool function invocation
**Root Tests**
- **test_cli.py**: CLI entry point
## Credits
This project is a Python port of the [Sequential Thinking MCP Server](https://github.com/modelcontextprotocol/servers/tree/main/src/sequentialthinking) by Anthropic, part of the Model Context Protocol servers collection. The original implementation provides the foundation for structured thinking and problem-solving.
## New Features
While maintaining full compatibility with the original design, UltraThink adds several enhancements:
1. **Confidence Scoring** - Explicit uncertainty tracking with 0.0-1.0 scale for each thought
2. **Auto-assigned Thought Numbers** - Optional thought numbering (auto-increments if omitted)
3. **Multi-Session Support** - Manage multiple concurrent thinking sessions with session IDs
4. **Assumption Tracking** - Make reasoning transparent with explicit assumptions, dependencies, and invalidation tracking
## License
[MIT](LICENSE)
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The tool 'ultrathink' has a clearly defined purpose for complex reasoning tasks, and no other tools exist to cause ambiguity.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'ultrathink' follows a single, consistent pattern with no deviations or mixing of conventions.
A single tool is too few for the server's apparent purpose of dynamic and reflective problem-solving, as it suggests a monolithic design that may lack granularity. Typically, such a domain would benefit from multiple specialized tools (e.g., for different reasoning phases or problem types), making this count borderline insufficient.
The tool surface is severely incomplete for the domain of complex reasoning. While 'ultrathink' covers multi-step analysis, there are obvious gaps such as tools for validating assumptions, summarizing insights, or handling specific sub-tasks like debugging or optimization separately, limiting agent flexibility.