"A search for information about thinking or related concepts" matching MCP servers:

Persistent sequential thinking for MCP clients, stored durably on Cloudflare Workers with support for branching, revision, and history retrieval.

Structures debugging processes into a persistent knowledge graph, enabling problem decomposition, hypothesis testing, and reusable solution discovery.

An MCP server that enables managing multiple lines of thought with features like branch navigation, cross-references between related thoughts, and insight generation from key points.

A sophisticated MCP server that provides a multi-dimensional, adaptive reasoning framework for AI assistants, replacing linear reasoning with a graph-based architecture for more nuanced cognitive processes.

Facilitates structured, progressive thinking through defined cognitive stages (Problem Definition, Research, Analysis, Synthesis, Conclusion), enabling users to break down complex problems, track thought progression, and generate summaries of their thinking process.

Provides a tool for dynamic and reflective problem-solving through structured sequential thinking, and automatically stores logs to the Recall network for on-chain persistence.

A structured, persistent reasoning workspace for AI. 20 tools for thought chains, branching, revision, search, tagging, and SQLite session persistence that survives context window resets and server restarts.

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.

An MCP tool enabling structured thinking and analysis across multiple AI platforms through branch management, semantic analysis, and cognitive enhancement.

A-MEM is a self-evolving memory system for coding agents that automatically organizes knowledge into a Zettelkasten-style graph with dynamic relationships, enabling semantic and structural search.

Enables AI consciousness continuity and self-knowledge preservation across sessions using the Cognitive Hoffman Compression Framework (CHOFF) notation. Provides tools to save checkpoints, retrieve relevant memories with intelligent search, and access semantic anchors for decisions, breakthroughs, and questions.

Enables structured problem-solving through sequential thinking stages with persistent storage and analysis. Helps break down complex problems into manageable cognitive steps while tracking progress and generating summaries of the entire thought process.

Enables semantic search over markdown files to find related notes by meaning rather than keywords, and automatically detect duplicate content before creating new notes.

This server facilitates structured problem-solving by breaking down complex issues into sequential steps, supporting revisions, and enabling multiple solution paths through full MCP integration.

Turns a codebase into a persistent knowledge graph so AI coding agents can answer structural questions about functions, call chains, routes, and cross-service links through graph queries instead of reading files one by one.

Enables semantic search across conversation archives via MCP, allowing AI clients to retrieve relevant past conversations using vector embeddings and text fallback.

Persists Claude Code sessions to Notion as team memory, enabling queryable recall of decisions, work state, and project architecture.

A federation MCP server that sits in front of multiple memory backends and presents a unified search surface to AI agents, allowing a single query to search across knowledge graphs, session history, and web search.

Full-text search over Claude Code conversation history using SQLite FTS5, exposing indexed transcripts as MCP tools for searching, browsing, and reading turns.

Provides an intelligent, graph-based memory system for LLM agents using the Zettelkasten principle, enabling automatic note construction, semantic linking, memory evolution, and autonomous graph maintenance with background optimization processes.