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

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

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 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 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.

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

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.

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.

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.

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 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.

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

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 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.

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

An MCP server that brings difficulty-adaptive, multi-path reasoning to Claude Code. It implements the Actor-Critic-Planner-Reflexion (ACPR) pipeline for deep research synthesis.

Enables AI agents to drive policy evaluation over MCP by starting executions, answering policy questions from case evidence, and reading deterministic outcomes with reasoning traces, while the runtime controls which questions are asked and how answers combine.

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.

Slipstream - Built by Vektor Memory - a persistent multi-layered memory architecture. 4-layer associative graph memory (MAGMA) with autonomous REM cycle, CLI, DXT-MCP Cloak Tools.

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

Strategic BDI cognitive reasoning engine for AI agents. Goal decomposition DAGs, multi-factor utility scoring, risk evaluation, and adaptive replanning over MCP.

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

A 66-tool reasoning pipeline that intercepts prompts to classify intent, check past mistakes, and generate execution plans, enabling any LLM to think harder and avoid repeating errors.

Provides AI agents with deterministic symbolic math capabilities through 21 tools for algebra, calculus, transforms, and DSP, preventing hallucinations by delegating computations to a SymPy engine.

A Model Context Protocol server that enhances Claude in Cursor AI with advanced reasoning capabilities including Monte Carlo Tree Search, Beam Search, R1 Transformer, and Hybrid Reasoning methods.

MCP server providing durable Substrate organizational memory tools, including search, read, query, ingest, remember, and sync. It integrates with Codex to automatically capture completed turns and session boundaries for persistent memory.

Turns Claude Code into a recursive analysis engine that builds explorable decision trees from IF/THEN chains, enabling thorough problem exploration and structured synthesis of action plans.