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

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.

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.

Enables agents to evaluate multi-turn episodic memory buffers, compute multi-factor salience scores, and deterministically prune low-utility noise so retained context fits within model limits.

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.

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.

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.

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.

Enables MCP clients to offload sub-problems to fresh, isolated contexts via sampling, offering plan, think_aside, and run_plan tools for sequential reasoning without cluttering the main conversation with verbose intermediate steps.

A Model Context Protocol (MCP) server implementation for the Google Gemini language model. This server allows Claude Desktop users to access the powerful reasoning capabilities of Gemini-2.0-flash-thinking-exp-01-21 model.

An advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.

Enables AI agents to achieve production-ready solutions through iterative refinement and recursive thinking processes. It features token optimization via context compression and session-based tracking to improve problem-solving depth while minimizing cost.

Provides Dify apps with a secure, app-scoped MCP tool catalog and action gateway for authorized FlowFlox operations, enabling intent-driven planning and fixed operational processes without exposing global credentials.

Enables AI agents to compress retrieved web documents against a query, pruning irrelevant noise so only the most relevant context reaches the model. It runs as a zero-dependency Python MCP server that accepts a query payload and returns structured, scored output.

This MCP server enables clients to reduce oversized prompts by selecting task-relevant context, removing lexical duplicates, and pruning to a token budget while preserving critical system invariants.

Enables thinking models to extend their reasoning by outsourcing parts of the chain of thought to a non-thinking model via the chat_agent tool, with configurable parameters.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Enables MCP-compatible agents to audit reasoning trajectories for loops, deadlocks, and logical contradictions and to perform non-destructive checkpoint rollbacks.

An advanced contextual memory server for agent orchestration providing chronological history, semantic vector search via Redis Stack, and temporary scratchpad storage. It also enables context compression through history rollups and facilitates inter-agent communication using a Redis-based Pub/Sub system.

Enables ZCode to control a real Chrome browser, including tabs, navigation, page content, clicks, form filling, keyboard input, scrolling, screenshots, and JavaScript evaluation through a local MCP bridge.

Enables ChatGPT to invoke native OpenClaw browser operations directly through an MCP connection, forwarding actions to the local Gateway while preserving runtime errors and providing a local call monitor.

A passive MCP server that exposes a toolbox of executable tools (shell, network, HTTP, AI search, SSH, S3 file operations) to autonomous agents via Streamable HTTP, with strong security features including Docker sandboxing and WAF.

Wraps a dependency-free memory lifecycle engine as six read-only MCP tools, letting any agent extract facts with pure regex, score signals, compute Ebbinghaus decay and archival judgments, and simulate a full five-stage dream cycle for long-term memory growth. Runs over stdio or streamable HTTP with no API key, storing no caller data.

Enables OpenAI-compatible clients to access external tools, APIs, repositories, and data sources through MCP with allowlisting, approval, and argument validation, deployable as a FastAPI service or Python library.

A shared memory and coordination server for multiple AI coding agents, built on the Model Context Protocol (MCP).

Provides MCP tools for persistent, cross-session memory via Cloudflare Agent Memory, enabling agents to store, recall, ingest, summarize, and manage memory namespaces.

Transforms any API into an intelligent MCP agent in seconds, enabling natural language interaction with APIs through the Model Context Protocol.

Proxies every Playwright MCP tool so agents drive a real browser, while automatically attaching per-site lessons, self-firing fixes, saved procedures and the site's observed internal API to each navigation. Later sessions on the same domain start where the previous one stopped, without anyone having to write or request the notes.

An MCP (Model Context Protocol) server implementation using HTTP SSE (Server-Sent Events) connections with built-in utility tools including echo, time, calculator, and weather query functionality.

Enables Claude Code to escalate complex coding problems to Google's Gemini AI for large-context execution tracing, cross-system impact analysis, performance diagnostics, hypothesis testing, and iterative AI-to-AI debugging conversations.

Enables AI tools and agents to share a persistent memory space, so a decision, task, or encrypted secret saved in one client can be searched, recalled, and handed off from another. It also tracks goals day by day, moves unfinished work between sessions, and keeps exports, trash recovery, and per-account isolation under the user's control.