Sequential Thinking MCP Server

A Model Context Protocol (MCP) server that facilitates structured, progressive thinking through defined stages. This tool helps break down complex problems into sequential thoughts, track the progression of your thinking process, and generate summaries.

Features

• Structured Thinking Framework: Organizes thoughts through standard cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion)

• Revisions & Branching: Revise earlier thoughts or fork alternative lines of reasoning, with revision- and branch-aware analysis and summaries

• Thought Tracking: Records and manages sequential thoughts with metadata

• Related Thought Analysis: Identifies connections between similar thoughts

• Progress Monitoring: Tracks your position in the overall thinking sequence

• Summary Generation: Creates concise overviews of the entire thought process

• Persistent Storage: Append-only JSONL session log with thread-safety and automatic crash recovery

• Data Import/Export: Share and reuse thinking sessions

• Extensible Architecture: Easily customize and extend functionality

• Robust Error Handling: Graceful handling of edge cases and corrupted data

• Type Safety: Comprehensive type annotations and validation

Related MCP server: Sequential Thinking MCP Server

Prerequisites

• Python 3.10 or higher

• UV package manager (Install Guide)

Key Technologies

• Pydantic: For data validation and serialization

• Portalocker: For thread-safe file access

• FastMCP: For Model Context Protocol integration

Project Structure

mcp-sequential-thinking/
├── mcp_sequential_thinking/
│   ├── server.py       # Main server implementation and MCP tools
│   ├── models.py       # Data models with Pydantic validation
│   ├── storage.py      # Thread-safe persistence layer
│   ├── storage_utils.py # Shared utilities for storage operations
│   ├── analysis.py     # Thought analysis and pattern detection
│   ├── utils.py        # Common utilities and helper functions
│   ├── logging_conf.py # Centralized logging configuration
│   └── __init__.py     # Package initialization
├── tests/              
│   ├── test_analysis.py # Tests for analysis functionality
│   ├── test_models.py   # Tests for data models
│   ├── test_storage.py  # Tests for persistence layer
│   └── __init__.py
├── run_server.py       # Server entry point script
├── debug_mcp_connection.py # Utility for debugging connections
├── README.md           # Main documentation
├── CHANGELOG.md        # Version history and changes
├── example.md          # Customization examples
├── LICENSE             # MIT License
└── pyproject.toml      # Project configuration and dependencies

Quick Start

The package is published on PyPI as mcp-sequential-thinking. The easiest way to run it is via uvx — no install step needed:

uvx mcp-sequential-thinking

Or install it permanently:

pip install mcp-sequential-thinking
mcp-sequential-thinking

Development Setup

To work on the code, clone the repository and set it up from source:

1. Set Up Project

   # Create and activate virtual environment
   uv venv
   .venv\Scripts\activate  # Windows
   source .venv/bin/activate  # Unix
   
   # Install package and dependencies
   uv pip install -e .
   
   # For development with testing tools
   uv pip install -e ".[dev]"
   
   # For all optional dependencies
   uv pip install -e ".[all]"

2. Run the Server

   # Run directly
   uv run -m mcp_sequential_thinking.server
   
   # Or use the installed script
   mcp-sequential-thinking

3. Run Tests

   # Run all tests
   pytest
   
   # Run with coverage report
   pytest --cov=mcp_sequential_thinking

Claude Desktop Integration

Add to your Claude Desktop configuration:

• Linux: ~/.config/Claude/claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

Option 1: Using uvx with the PyPI package (recommended)

No clone, no venv, no manual updates — uvx fetches the package from PyPI and runs it:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

To test unreleased changes, point uvx at the repository instead:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": [
        "--from",
        "git+https://github.com/arben-adm/mcp-sequential-thinking",
        "mcp-sequential-thinking"
      ]
    }
  }
}

Option 2: Using the installed entry point

If you've installed the package with pip install mcp-sequential-thinking (or pip install -e . from a clone):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "mcp-sequential-thinking"
    }
  }
}

Option 3: Using a local clone's virtual environment (development)

If you have set up the project with uv venv && uv pip install -e ., point directly to the venv Python interpreter. This avoids dependency resolution issues (e.g., on systems with Python 3.14+):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "/path/to/mcp-sequential-thinking/.venv/bin/python",
      "args": [
        "-m",
        "mcp_sequential_thinking.server"
      ],
      "cwd": "/path/to/mcp-sequential-thinking"
    }
  }
}

Option 4: Using uv run on a local clone (development)

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/path/to/mcp-sequential-thinking",
        "-m",
        "mcp_sequential_thinking.server"
      ]
    }
  }
}

Editor & IDE Integration

Cursor

Add to your Cursor MCP configuration at .cursor/mcp.json in your project root (or globally at ~/.cursor/mcp.json):

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

VS Code (Copilot MCP)

VS Code supports MCP servers since version 1.99+. Add to .vscode/mcp.json in your workspace or to your user settings.json:

{
  "servers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Note: Enable MCP support in VS Code via "chat.mcp.enabled": true in your settings.

Zed

Add to your Zed settings (~/.config/zed/settings.json):

{
  "context_servers": {
    "sequential-thinking": {
      "command": {
        "path": "uvx",
        "args": ["mcp-sequential-thinking"]
      }
    }
  }
}

Claude Code (CLI)

Add the server using the CLI:

claude mcp add sequential-thinking -- uvx mcp-sequential-thinking

Or manually create/edit .mcp.json in your project root:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Windsurf

Add to your Windsurf MCP configuration at ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "sequential-thinking": {
      "command": "uvx",
      "args": ["mcp-sequential-thinking"]
    }
  }
}

Gemini CLI

Add to your Gemini CLI settings at ~/.gemini/settings.json:

{
  "mcpServers": {
    "sequential-thinking": {
      "type": "stdio",
      "command": "uvx",
      "args": ["mcp-sequential-thinking"],
      "env": {}
    }
  }
}

Tip: All editor configurations above run the published PyPI package via uvx. To run from a local clone instead (e.g. for development), use uv run --directory /path/to/mcp-sequential-thinking -m mcp_sequential_thinking.server or point directly to the venv Python interpreter (see Claude Desktop Options 3 and 4).

How It Works

The server maintains a history of thoughts and processes them through a structured workflow. Each thought is validated using Pydantic models, categorized into thinking stages, and stored with relevant metadata in a thread-safe storage system. The server automatically handles data persistence, backup creation, and provides tools for analyzing relationships between thoughts.

Sessions are persisted as an append-only JSONL log at ~/.mcp_sequential_thinking/current_session.jsonl (override the directory with the MCP_STORAGE_DIR environment variable). Each process_thought call appends a single fsynced line, so the file doubles as an audit trail and a truncated final line from an interrupted write is recovered automatically. Sessions from v0.5.x (current_session.json) are migrated losslessly on first start; the original file is kept as current_session.json.migrated-to-v2.

Usage Guide

The Sequential Thinking server exposes five main tools:

1. process_thought

Records and analyzes a new thought in your sequential thinking process.

Parameters:

• thought (string): The content of your thought

• thought_number (integer): Position in your sequence (e.g., 1 for first thought)

• total_thoughts (integer): Expected total thoughts in the sequence

• next_thought_needed (boolean): Whether more thoughts are needed after this one

• stage (string): The thinking stage - must be one of:

  • "Problem Definition"

  • "Research"

  • "Analysis"

  • "Synthesis"

  • "Conclusion"

• tags (list of strings, optional): Keywords or categories for your thought

• axioms_used (list of strings, optional): Principles or axioms applied in your thought

• assumptions_challenged (list of strings, optional): Assumptions your thought questions or challenges

• is_revision (boolean, optional): Whether this thought revises an earlier one

• revises_thought_number (integer, optional): The number of the earlier thought being revised (required together with is_revision)

• branch_from_thought (integer, optional): The thought number to fork from when exploring an alternative path

• branch_id (string, optional): Identifier for the branch (letters, digits, -, _; max 64 characters; requires branch_from_thought)

Example:

# First thought in a 5-thought sequence
process_thought(
    thought="The problem of climate change requires analysis of multiple factors including emissions, policy, and technology adoption.",
    thought_number=1,
    total_thoughts=5,
    next_thought_needed=True,
    stage="Problem Definition",
    tags=["climate", "global policy", "systems thinking"],
    axioms_used=["Complex problems require multifaceted solutions"],
    assumptions_challenged=["Technology alone can solve climate change"]
)

# Revise an earlier thought
process_thought(
    thought="Framing the problem purely around emissions was too narrow; adaptation matters equally.",
    thought_number=6,
    total_thoughts=6,
    next_thought_needed=True,
    stage="Problem Definition",
    is_revision=True,
    revises_thought_number=1
)

# Fork an alternative line of reasoning
process_thought(
    thought="What if we approach this from a market-incentive angle instead?",
    thought_number=7,
    total_thoughts=7,
    next_thought_needed=True,
    stage="Analysis",
    branch_from_thought=3,
    branch_id="market-incentives"
)

2. generate_summary

Generates a summary of your entire thinking process.

Example output:

{
  "summary": {
    "totalThoughts": 5,
    "stages": {
      "Problem Definition": 1,
      "Research": 1,
      "Analysis": 1,
      "Synthesis": 1,
      "Conclusion": 1
    },
    "timeline": [
      {"number": 1, "stage": "Problem Definition"},
      {"number": 2, "stage": "Research"},
      {"number": 3, "stage": "Analysis"},
      {"number": 4, "stage": "Synthesis"},
      {"number": 5, "stage": "Conclusion"},
      {"number": 6, "stage": "Problem Definition", "isRevision": true},
      {"number": 7, "stage": "Analysis", "branchId": "market-incentives"}
    ],
    "branches": {
      "market-incentives": {"fromThought": 3, "thoughtCount": 1}
    },
    "revisionCount": 1
  }
}

3. clear_history

Resets the thinking process by clearing all recorded thoughts.

4. export_session

Exports the current thinking session to a JSON file for sharing or backup.

Parameters:

• file_path (string): Path to the output JSON file. Since v0.6.0, exports are confined to the exports/ subdirectory of the storage directory; relative paths resolve to ~/.mcp_sequential_thinking/exports/ and parent directories are created automatically.

Example:

export_session(file_path="my-analysis.json")
# -> written to ~/.mcp_sequential_thinking/exports/my-analysis.json

5. import_session

Imports a previously exported thinking session from a JSON file. Exports created with v0.5.x remain importable.

Parameters:

• file_path (string): Path to the JSON file to import. Like exports, resolved inside the exports/ subdirectory of the storage directory.

Comparison to the official sequential-thinking server

The official MCP sequential-thinking server provides the core paradigm: numbered thoughts with revisions and branching, held in memory for the duration of the process. This server implements the same paradigm and adds:

• Persistence: sessions survive restarts (append-only JSONL log with crash recovery and automatic migration), and can be exported, shared and re-imported as JSON.

• Thinking stages: thoughts are categorized into cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), enabling stage-based filtering and completeness checks.

• Analysis: related-thought detection via stages and tags, per-thought progress, and rich summaries including branch and revision statistics.

If you only need ephemeral chain-of-thought scaffolding, the official server is a lighter choice; if you want durable, analyzable thinking sessions, this one is built for that.

Practical Applications

• Decision Making: Work through important decisions methodically

• Problem Solving: Break complex problems into manageable components

• Research Planning: Structure your research approach with clear stages

• Writing Organization: Develop ideas progressively before writing

• Project Analysis: Evaluate projects through defined analytical stages

Getting Started

With the proper MCP setup, simply use the process_thought tool to begin working through your thoughts in sequence. As you progress, you can get an overview with generate_summary and reset when needed with clear_history.

Customizing the Sequential Thinking Server

For detailed examples of how to customize and extend the Sequential Thinking server, see example.md. It includes code samples for:

• Modifying thinking stages

• Enhancing thought data structures with Pydantic

• Adding persistence with databases

• Implementing enhanced analysis with NLP

• Creating custom prompts

• Setting up advanced configurations

• Building web UI integrations

• Implementing visualization tools

• Connecting to external services

• Creating collaborative environments

• Separating test code

• Building reusable utilities

License

MIT License