Promptly Technologies LLC mcp structured thinking
by Promptly-Technologies-LLC
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
What is Promptly Technologies LLC mcp structured thinking
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
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Frequently Asked Questions
What is MCP?
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to LLMs. Think of MCP like a USB-C port for AI applications, providing a standardized way to connect AI models to different data sources and tools.
What are MCP Servers?
MCP Servers are lightweight programs that expose specific capabilities through the standardized Model Context Protocol. They act as bridges between LLMs like Claude and various data sources or services, allowing secure access to files, databases, APIs, and other resources.
How do MCP Servers work?
MCP Servers follow a client-server architecture where a host application (like Claude Desktop) connects to multiple servers. Each server provides specific functionality through standardized endpoints and protocols, enabling Claude to access data and perform actions through the standardized protocol.
Are MCP Servers secure?
Yes, MCP Servers are designed with security in mind. They run locally with explicit configuration and permissions, require user approval for actions, and include built-in security features to prevent unauthorized access and ensure data privacy.
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