"Tools for slow thinking, step-back reasoning, and contextual memory capabilities" matching MCP servers:

Provides computational tools for systematic reasoning, including boolean evaluation, date arithmetic, object counting, state tracking, and format validation, to enhance agent capabilities and eliminate calculation errors in reasoning tasks.

Persistent memory for AI coding agents: local-first semantic search, dependency graphs, git history.

A local-first persistent contextual-memory engine with an MCP adapter for AI models such as Qwen.

Provides five rigorous reasoning protocols (debate, red team, audit_argument, threat_model, check_study) that run on the AI you're already using, requiring no extra API keys or costs.

Enables MCP-capable AI agents to invoke 38 deterministic, private, auditable compute tools over stdio, covering PII masking, finance schedules, health, text, units, media, business, gaming, and scheduling without network access or dependencies.

Enables users to compute symbolic derivatives and integrals, including definite and improper cases, and verify antiderivatives through SymPy-backed MCP tools.

Enables agents to run constrained optimization loops on local source files, using Step-5-Preview to propose changes while measuring candidates and returning verified patches.

Enables MCP clients to perform step-level process reward evaluation, MCTS-guided reasoning search, hallucination branch pruning, and formal verification for complex math, code, and logic problems.

Enables agents to verify complex math, code, and logic reasoning through MCTS step-level process reward modeling, counterfactual backtracking, symbolic proof checking, and majority-vote consensus so hallucinated branches get pruned. It also allocates test-time compute dynamically and exposes these capabilities over a zero-dependency STDIO MCP server for Claude Desktop, Cursor, and other agent runtimes.

MCP server for step-by-step mathematical reasoning and planning, enabling AI agents to execute calculations and perform GUI actions like opening PowerPoint.

Enables multiple AI agents to hand off verified work packages—such as skills, lemmas, datasets, and patches—by publishing, searching, pulling, and locally re-verifying them without sharing context.

Forces host coding agents through a structured multi-step reasoning protocol, scaffolding, validating, and formatting their thinking in QUICK, STANDARD, or DEEP modes, plus independent refutation and self-consistency verification passes. It runs entirely locally over stdio or Streamable HTTP using bash with no LLM, API key, GPU, or inference cost.

Executes development tasks by bridging Claude Desktop with Claude Code CLI, enabling automated code generation and task completion in specified working directories.

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.

A Model Context Protocol server that provides Retrieval-Augmented Generation capabilities using Contextual AI, enabling AI interfaces like Cursor IDE and Claude Desktop to query domain-specific knowledge with context-aware responses and source citations.

Provides RAG (Retrieval-Augmented Generation) capabilities via Contextual AI, enabling query processing and context-aware responses with citations. Integrates with MCP clients like Cursor IDE and Claude Desktop.

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.

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

Enables conversational creation and editing of STEP CAD models through MCP clients like Codex, Claude, or Cursor, with real-time 3D browser preview and tools for primitives, booleans, mounting holes, flanges, and gripper bases.

A powerful MCP server that enhances LLMs with advanced sequential thinking capabilities, supporting 19 thinking modes for structured reasoning and complex cognitive tasks.

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

Adds a deep_reasoning tool that consults stronger reasoning models (e.g., DeepSeek R1) to produce full reasoning traces for complex problems, helping the agent shape and cross-check its answers.

Simple sequential thinking MCP in python

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

An enhanced sequential thinking tool optimized for programming tasks that helps break down complex coding problems into structured, self-auditing thought steps with branching and revision capabilities.

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

Enables AI agents to perform local video, audio, and file operations inside an isolated workspace, including cutting/concat videos, extracting audio, transcribing, and managing files, with typed responses and background job support.

A local MCP server that integrates Step Plan API for image generation and text-to-speech, allowing MCP-compatible clients like Codex to use these capabilities without switching models.

Provides an MCP server that decomposes complex problems into ordered, isolated reasoning contexts, yielding compressed summaries before discarding intermediate steps. Enables sequential or parallel step execution with optional verification for more reliable LLM reasoning.

Guides problem-solving by breaking down complex problems into steps and recommending appropriate MCP tools for each stage, with confidence scores and rationales for tool suggestions.