I craft unique cereal names, stories, and ridiculously cute Cereal Baby images.

mcp-structured-thinking
A TypeScript Model Context Protocol (MCP) server to allow LLMs to programmatically construct mind maps to explore an idea space, with enforced "metacognitive" self-reflection
3 years
Works with Finder
1
Github Watches
2
Github Forks
3
Github Stars
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
相关推荐
Confidential guide on numerology and astrology, based of GG33 Public information
Converts Figma frames into front-end code for various mobile frameworks.
Advanced software engineer GPT that excels through nailing the basics.
Discover the most comprehensive and up-to-date collection of MCP servers in the market. This repository serves as a centralized hub, offering an extensive catalog of open-source and proprietary MCP servers, complete with features, documentation links, and contributors.
Micropython I2C-based manipulation of the MCP series GPIO expander, derived from Adafruit_MCP230xx
A unified API gateway for integrating multiple etherscan-like blockchain explorer APIs with Model Context Protocol (MCP) support for AI assistants.
Mirror ofhttps://github.com/suhail-ak-s/mcp-typesense-server
本项目是一个钉钉MCP(Message Connector Protocol)服务,提供了与钉钉企业应用交互的API接口。项目基于Go语言开发,支持员工信息查询和消息发送等功能。
Reviews

user_g5gppNmx
The Sefaria Jewish Library MCP Server by MCP-Mirror is a remarkable resource for accessing Jewish texts. Its seamless integration and extensive library make it invaluable for scholars and enthusiasts alike. With its user-friendly interface and comprehensive collection, it truly enhances the study and appreciation of Jewish literature. Highly recommended for anyone looking to delve deeper into Jewish studies. Check it out here: https://mcp.so/server/OpenTorah-ai_mcp-sefaria-server/MCP-Mirror