"An MCP server for structured problem-solving and reflective thinking" matching MCP servers:

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.

MCP server providing managed persistent memory for AI agents. Read and write structured state across sessions, tools, and restarts at 1000+ requests per second, with no infrastructure to self-host or operate.

Enables dynamic and reflective problem-solving through a structured thinking process with tools for sequential analysis, tree of thoughts, self-critique, and integration with external knowledge and codebase analysis.

Remote MCP server for dynamic and reflective problem-solving through structured thinking. Enables step-by-step reasoning with branching, revision tracking, and session management.

An MCP server that enables Claude to break down complex problems into manageable steps with support for revision and branching, facilitating dynamic and reflective problem-solving through a structured thinking process.

Provides AI systems with structured thinking frameworks and reasoning tools to maintain consistent problem-solving patterns across conversations. Enables multi-step reasoning, decision analysis, and systematic troubleshooting through invocable mental models.

Enables any MCP client to surface twelve structured thinking frameworks for clarifying questions, learning, problem-solving, decision-making, and self-reflection, with fillable slash commands for humans and programmatic retrieval for agents.

Facilitates structured creative thinking through sequential thought processing, helping users shift from reactive problem-solving to proactive outcome creation using structural tension analysis and stage-based thinking workflows.

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.

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

Enables AI agents to perform dynamic and reflective problem-solving through a chain of thoughts, allowing them to break down complex problems, revise past thoughts, and explore logic branches before reaching a conclusion.

Provides systems thinking models as composable analysis lenses for architecture, infrastructure, DevOps, incident analysis, and technical decision-making.

Provides a comprehensive creative thinking tool with methods like structured process, perspective shift, and constraint-based innovation to enhance divergent thinking.

Provides a sequentialthinking tool for dynamic, reflective problem-solving via chain-of-thought reasoning. Supports local Stdio and remote SSE deployment on Google Cloud Run.

Enables automated export, compression, and cloud upload of Figma design assets by simply providing Figma links. Supports batch processing of nodes and children, returning structured data with publicly accessible image URLs.

An enhanced MCP server that enables users to interact with NotebookLM while ensuring strict source fidelity and citation accuracy through client-side prompt structuring. It guides Claude to transform user queries into structured prompts that prevent the addition of external knowledge during document analysis.

A tool that implements Claude Shannon's problem-solving methodology to help break down complex problems into structured steps including problem definition, constraints, modeling, validation, and implementation.

Enforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.

Make it easy for agents to build their context about your projects over time The server provides a set of tools to help agents accumulate knowledge about a project over time in a structured way.

Enables MCP clients to extract validated typed records from unstructured text using named schemas, with schema listing and extraction tools. It returns validated JSON or tool errors, optionally using an offline parser or an OpenAI-compatible model with retries on validation failures.