How are thought quality scores used?
Each thought is assigned a quality score between 0 and 1, which combines with the thought's stage to provide metacognitive feedback that steers the LLM's thinking process.
@Promptly-Technologies-LLC
基本信息
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
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"structured-thinking": {
"command": "npx",
"args": [
"-y",
"structured-thinking"
]
}
}
}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.
A TypeScript Model Context Protocol (MCP) server that allows LLMs to programmatically construct mind maps for exploring an idea space, with enforced metacognitive self-reflection. It is based on Arben Ademi's Sequential Thinking Python server.
Configure the tool in Claude Desktop, Cursor, or another MCP client using the JSON setting with "command": "npx" and "args": ["-y", "structured-thinking"]. The server exposes MCP tools such as capture_thought, revise_thought, retrieve_relevant_thoughts, get_thinking_summary, and clear_thinking_history.
Each thought is assigned a quality score between 0 and 1, which combines with the thought's stage to provide metacognitive feedback that steers the LLM's thinking process.
Each thought is tagged with a stage (e.g., Problem Definition, Analysis, Ideation). If the LLM spends too long in a stage or has low-quality thoughts, the server gives feedback to steer toward other stages or different thinking strategies.
No. Currently, all thoughts are stored only in memory. There is no user interface or persistence to a file or database.
The metacognitive monitoring is mechanical, based on a single self-reported quality score and naive stage-based multipliers. There is also no user interface or visualization of the thought graph.
It exposes `capture_thought`, `revise_thought`, `retrieve_relevant_thoughts`, `get_thinking_summary`, and `clear_thinking_history`.
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