"A server for finding resources about deep thinking and critical thinking skills" matching MCP servers:

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

This is a MCP server that implements an unreasonable thinking system. It helps generate unconventional solutions: Generating unreasonable thoughts that challenge conventional wisdom. Creating branches of thinking in different directions

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.

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

An MCP server that enhances sequential thinking with Ultra Think methodology, enabling structured, step-by-step reasoning with quality metrics, bias detection, and resource management.

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.

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.

Enables deep reasoning and cognitive enhancement through multi-agent debate, bias detection, and structured thinking, with privacy-first local execution.

Facilitates structured, step-by-step problem-solving using the Model Context Protocol, ideal for planning complex tasks like supply chain management.

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.

Transform any AI agent into a domain expert by giving it access to modular, reusable skills through the Model Context Protocol. Brings Claude's Skills format to any MCP-compatible agent, allowing you to create skills once and use them everywhere.

Enforces 21 critical rules derived from 96+ documented failure patterns to prevent common AI assistant mistakes. Provides automatic compliance checklists, rule summaries, and auto-updates.

Web scraper and token compressor that converts HTML to clean markdown with 70-80% fewer tokens. Single-page compression and multi-page BFS crawling with auto-fallback fetch modes.

MCP server that demonstrates the Resources feature by exposing static and dynamic resources, including contact data and personalized greetings, through MCP.

Enables MCP clients to load eight on-demand coding skills and inspect project repositories through bounded, read-only map, search, read, diff, checks, and style tools. It keeps context lean by loading skill bodies only when needed and limiting repository scan and output sizes.

Enables AI-to-AI consultation for critical thinking and complex reasoning via OpenRouter, allowing one AI to delegate tasks to another AI model.

A multi-agent system for code rules management using MCP, supporting search, generation, and architectural validation through specialized agents.

A multi-agent research system that decomposes complex queries into targeted sub-questions, searches the web in parallel, scores source credibility, and synthesizes findings into structured markdown reports.

This MCP server enables AI agents to interact with Ripar's decentralized registries on Algorand, offering tools for searching agents, checking reputations, listing jobs, auditing settlements, and composing unsigned transactions for payments.

Enables AI assistants to search a library of over 20,000 open legal skills by keyword, by meaning, or a hybrid of both, filtering by jurisdiction, language, practice area, or tag. It also returns facet counts for the collection and retrieves the full text of any selected skill.

Enables MCP hosts to discover, browse, and load Agent Skills from one or many SKILL.md libraries over the Model Context Protocol, with namespaced libraries, search/list/get tools, progressive disclosure, and a digest-verified pull client for syncing skills to disk.

Open-source skills that empower any AI agent (Claude, Cursor, Codex, Hermes, etc.) to generate end-to-end marketing campaigns — UGC videos, ad videos, product photography and more. From a product photo and a brief it casts AI actors, picks the best models, and edits finished assets of any length with consistent actor and product. It also researches competitors, publishes, and reviews performance.

MCP connector for the ok-wiki knowledge base, enabling search, read, create, edit, history, tags, and asset management via natural language.

Exposes 79 cybersecurity skills and 12 orchestrator agents over MCP, with a typed 11-field output contract, an enforced resolvable-evidence gate (no verdict without a resolvable source), and human-approval gating for every mutating action. Apache-2.0, stdlib-only.

A package manager for AI agents that connects LLMs to a global registry of capabilities, allowing them to autonomously discover, install, and learn new skills from a centralized repository.

Enables agents to discover, read, and use reusable skills stored in public GitHub repositories.

Enables users to create and refine a research outline, queue report generation in the background, and retrieve progress or completed Markdown reports, with automatic checkpointing so unfinished work can resume after a restart.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Serves a bundled greeting skill over stdio using the MCP Skills extension, letting clients discover the skill's metadata and digest, read its SKILL.md as an ordinary MCP resource, and invoke a greet tool that returns a personalized hello.

Enables AI tools to access local crypto market data, run analyses, perform 66-item deep research, search RAG context, and retrieve generated reports and prompts via MCP.