Structured Thinking

Structured Thinking

promptly-technologies-llc

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.

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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

AI developers building reasoning systemsResearchers studying AI decision-making processesComplex problem-solving workflows requiring structured thinkingApplications needing transparent AI reasoning trails
Metacognitive feedback systemAutomatic thought quality scoringShort-term and long-term memory management

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)

capture_thought

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.

revise_thought

Revises a thought in memory and in the thought history.

retrieve_relevant_thoughts

Finds thoughts from long-term storage that share tags with the specified thought.

get_thinking_summary

Generate a comprehensive summary of the entire thinking process.

clear_thinking_history

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 thought
  • thought_number: Current position in the sequence
  • total_thoughts: Expected total number of thoughts
  • next_thought_needed: Whether another thought should follow
  • stage: Current thinking stage (e.g., "Problem Definition", "Analysis")
  • is_revision (optional): Whether this revises a previous thought
  • revises_thought (optional): Number of thought being revised
  • branch_from_thought (optional): Starting point for a new thought branch
  • branch_id (optional): Identifier for the current branch
  • needs_more_thoughts (optional): Whether additional thoughts are needed
  • score (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

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