
Structured Thinking
Helps AI systems organize their reasoning by capturing thoughts in structured stages with quality scores and metacognitive feedback. Creates a trackable history of the thinking process with branching support for exploring multiple solution paths.
Structures reasoning processes through defined thought stages, managing a history of thoughts with metadata for transparent, step-by-step problem solving and decision making.
What it does
- Capture thoughts with quality scores and stage classifications
- Revise existing thoughts in the thinking history
- Retrieve relevant thoughts based on shared tags
- Generate comprehensive summaries of thinking processes
- Create thought branches for parallel reasoning paths
- Clear thinking history to reset state
Best for
About Structured Thinking
Structured Thinking is a community-built MCP server published by promptly-technologies-llc that provides AI assistants with tools and capabilities via the Model Context Protocol. Enhance decision making with structured reasoning and transparent, step-by-step problem solving, ideal for collaborative It is categorized under ai ml. This server exposes 5 tools that AI clients can invoke during conversations and coding sessions.
How to install
You can install Structured Thinking in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.
License
Structured Thinking is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Tools (5)
Stores a new thought in memory and in the thought history and runs a pipeline to classify the thought, return metacognitive feedback, and retrieve relevant thoughts.
Revises a thought in memory and in the thought history.
Finds thoughts from long-term storage that share tags with the specified thought.
Generate a comprehensive summary of the entire thinking process.
Clear all recorded thoughts and reset the server state.
Structured Thinking MCP Server
A TypeScript Model Context Protocol (MCP) server based on Arben Ademi's Sequential Thinking Python server. The motivation for this project is to allow LLMs to programmatically construct mind maps to explore an idea space, with enforced "metacognitive" self-reflection.
Setup
Set the tool configuration in Claude Desktop, Cursor, or another MCP client as follows:
{
"structured-thinking": {
"command": "npx",
"args": ["-y", "structured-thinking"]
}
}
Overview
Thought Quality Scores
When an LLM captures a thought, it assigns that thought a quality score between 0 and 1. This score is used, in combination with the thought's stage, for providing "metacognitive" feedback to the LLM how to "steer" its thinking process.
Thought Stages
Each thought is tagged with a stage (e.g., Problem Definition, Analysis, Ideation) to help manage the life-cycle of the LLM's thinking process. In the current implementation, these stages play a very important role. In effect, if the LLM spends too long in a given stage or is having low-quality thoughts in the current stage, the server will provide feedback to the LLM to "steer" its thinking toward other stages, or at least toward thinking strategies that are atypical of the current stage. (E.g., in deductive mode, the LLM will be encouraged to consider more creative thoughts.)
Thought Branching
The LLM can spawn “branches” off a particular thought to explore different lines of reasoning in parallel. Each branch is tracked separately, letting you manage scenarios where multiple solutions or ideas should coexist.
Memory Management
The server maintains a "short-term" memory buffer of the LLM's ten most recent thoughts, and a "long-term" memory of thoughts that can be retrieved based on their tags for summarization of the entire history of the LLM's thinking process on a given topic.
Limitations
Naive Metacognitive Monitoring
Currently, the quality metrics and metacognitive feedback are derived mechanically from naive stage-based multipliers applied to a single self-reported quality score.
As part of the future work, I plan to add more sophisticated metacognitive feedback, including semantic analysis of thought content, thought verification processes, and more intelligent monitoring for reasoning errors.
Lack of User Interface
Currently, the server stores all thoughts in memory, and does not persist them to a file or database. There is also no user interface for reviewing the thought space or visualizing the mind map.
As part of the future work, I plan to incorporate a simple visualization client so the user can watch the thought graph evolve.
MCP Tools
The server exposes the following MCP tools:
capture_thought
Create a thought in the thought history, with metadata about the thought's type, quality, content, and relationships to other thoughts.
Parameters:
thought: The content of the current thoughtthought_number: Current position in the sequencetotal_thoughts: Expected total number of thoughtsnext_thought_needed: Whether another thought should followstage: Current thinking stage (e.g., "Problem Definition", "Analysis")is_revision(optional): Whether this revises a previous thoughtrevises_thought(optional): Number of thought being revisedbranch_from_thought(optional): Starting point for a new thought branchbranch_id(optional): Identifier for the current branchneeds_more_thoughts(optional): Whether additional thoughts are neededscore(optional): Quality score (0.0 to 1.0)tags(optional): Categories or labels for the thought
revise_thought
Revise a thought in the thought history, with metadata about the thought's type, quality, content, and relationships to other thoughts.
Parameters:
thought_id: The ID of the thought to revise- Parameters from
capture_thought
retrieve_relevant_thoughts
Retrieve thoughts from long-term storage that share tags with the specified thought.
Parameters:
thought_id: The ID of the thought to retrieve relevant thoughts for
get_thinking_summary
Generate a comprehensive summary of the entire thinking process.
clear_thinking_history
Clear all recorded thoughts and reset the server state.
License
MIT
Alternatives
Related Skills
Browse all skillsInvoke IMMEDIATELY via python script when user requests structured reasoning for open-ended analytical questions. Do NOT explore first - the script orchestrates the thinking workflow.
Break down any problem with structured thinking, action plans, and progress tracking
Sequential Thinking MCP and UltraThink mode for deep analysis, complex problem decomposition, and structured reasoning workflows
Convert entire PDF documents to clean, structured Markdown for full context loading. Use this skill when the user wants to extract ALL text from a PDF into context (not grep/search), when discussing or analyzing PDF content in full, when the user mentions "load the whole PDF", "bring the PDF into context", "read the entire PDF", or when partial extraction/grepping would miss important context. This is the preferred method for PDF text extraction over page-by-page or grep approaches.
Guides Claude in writing idiomatic, efficient, well-structured Rust code using proper data modeling, traits, impl organization, macros, and build-speed best practices.
Convert various file formats (PDF, Office documents, images, audio, web content, structured data) to Markdown optimized for LLM processing. Use when converting documents to markdown, extracting text from PDFs/Office files, transcribing audio, performing OCR on images, extracting YouTube transcripts, or processing batches of files. Supports 20+ formats including DOCX, XLSX, PPTX, PDF, HTML, EPUB, CSV, JSON, images with OCR, and audio with transcription.