sequential-thinking-mcp
Simple sequential thinking MCP in python
Tools
think
Tool for advanced meta-cognition and dynamic reflective problem-solving via thought logging. Supports thread following, step-tracking, self-correction, and tool recommendations. For each new user message, begin a new thought thread and log each thought after each completed step. Key functionalities: - Agentic Workflow Orchestration: Guides through complex tasks by breaking them into precise, manageable, traceable steps. - Automatic smart thinking process: Avoids over-questionning users about their intention and just figures it out how to proceed. - Iterative Refinement: Assesses success of each step and self-corrects if necessary, adapting to new information or errors (failure, empty results, etc). - Tool Recommendation: Suggests relevantly specific available tools (`tool_recommendation`) to execute planned actions or gather necessary information. - Proactive Planning: Utilizes `left_to_be_done` for explicit future state management and task estimation. Args: - `thread_purpose` (str): A concise, high-level objective or thematic identifier for the current thought thread. Essential for organizing complex problem-solving trajectories. - `thought` (str): The detailed, atomic unit of reasoning or action taken by the AI agent at the current step. This forms the core of the agent's internal monologue. - `thought_index` (int): A monotonically increasing integer representing the sequence of thoughts within a specific `thread_purpose`. Crucial for chronological tracking and revision targeting. - `tool_recommendation` (str, optional): A precise actionable suggestion for the next tool to be invoked, omitted if no tool is needed, directly following the current thought. - `left_to_be_done` (str, optional): A flexible forward-looking statement outlining the next steps or sub-goals to be completed within the current `thread_purpose`. Supports multi-step planning and progress tracking. Omitted if no further action is needed. Example of thought process: 1) user: "I keep hearing about central banks, but I don't understand what they are and how they work." 2) think(thread_purpose="Central banks explained", thought="Requires information about central banks and how they work. Consider using <named_tool> tool.", thought_index=1, tool_recommendation="<named_tool>", left_to_be_done="Summarize the findings and create an exhaustive graph representation") 3) call <named_tool> 4) think(thread_purpose="Central banks explained", thought="Summary of the findings is clear and exhaustive, I have enough information. Must create the graph with <named_tool>.", thought_index=2, tool_recommendation="<named_tool>", left_to_be_done="Send summary and graph to the user") 5) call <named_tool> 6) final: respond with summary and graph (no need to call think since left_to_be_done is a simple final step)
README
Sequential Thinking MCP
This repository provides an MCP (Model Context Protocol) server that enables an AI agent to perform advanced meta-cognition and dynamic, reflective problem-solving.
<a href="https://glama.ai/mcp/servers/@philogicae/sequential-thinking-mcp"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@philogicae/sequential-thinking-mcp/badge?cache-control=no-cache" alt="Sequential Thinking MCP" /> </a>
Table of Contents
Features
- Advanced Meta-Cognition: Provides a
thinktool for dynamic and reflective problem-solving through thought logging. - Agentic Workflow Orchestration: Guides AI agents through complex tasks by breaking them into precise, manageable, and traceable steps.
- Iterative Refinement: Assesses the success of each step and self-corrects if necessary, adapting to new information or errors.
- Proactive Planning: Utilizes
left_to_be_donefor explicit future state management and task estimation. - Tool Recommendation: Suggests specific tools to execute planned actions or gather necessary information.
Setup
Prerequisites
- Python 3.10+
uv(for local development)
Installation
Choose one of the following installation methods.
Install from PyPI (Recommended)
This method is best for using the package as a library or running the server without modifying the code.
- Install the package from PyPI:
pip install sequential-thinking-mcp
- Run the MCP server:
python -m sequential_thinking
For Local Development
This method is for contributors who want to modify the source code.
Using uv:
- Clone the repository:
git clone https://github.com/philogicae/sequential-thinking-mcp.git
cd sequential-thinking-mcp
- Install dependencies using
uv:
uv sync
- Run the MCP server:
uv run -m sequential_thinking
Usage
As MCP Server
from sequential_thinking import mcp
mcp.run(transport="sse")
Via MCP Clients
Usable with any MCP-compatible client. Available tools:
think: Log a thought, plan next steps, and recommend tools.
Example with Windsurf
Configuration:
{
"mcpServers": {
...
# with stdio (only requires uv)
"sequential-thinking-mcp": {
"command": "uvx",
"args": [ "sequential-thinking-mcp" ]
},
# with sse transport (requires installation)
"sequential-thinking-mcp": {
"serverUrl": "http://127.0.0.1:8000/sse"
},
# with streamable-http transport (requires installation)
"sequential-thinking-mcp": {
"serverUrl": "http://127.0.0.1:8000/mcp" # not yet supported by every client
},
...
}
}
Changelog
See CHANGELOG.md for a history of changes to this project.
Contributing
Contributions are welcome! Please open an issue or submit a pull request.
License
This project is licensed under the MIT License - see the LICENSE file for details.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。
mcp-server-qdrant
这个仓库展示了如何为向量搜索引擎 Qdrant 创建一个 MCP (Managed Control Plane) 服务器的示例。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。