"Features of Grid Trading Strategies" matching MCP servers:

Provides 10 structured reasoning strategies (Chain of Thought, ReAct, Tree of Thoughts, etc.) for complex problem-solving with session persistence, branching, and tool integration capabilities.

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

AoT MCP server enables AI models to solve complex reasoning problems by decomposing them into independent, reusable atomic units of thought, featuring a powerful decomposition-contraction mechanism that allows for deep exploration of problem spaces while maintaining high confidence in conclusions.

Enables sophisticated reasoning workflows using graph-based representations for AI models.

A scientific reasoning framework that leverages graph structures and the Model Context Protocol (MCP) to process complex scientific queries through an Advanced Scientific Reasoning Graph-of-Thoughts (ASR-GoT) approach.

An MCP server for managing contextual data as markdown files with metadata, enabling agents to save, retrieve, search, and delete contexts using simple CRUD operations.

Provenance tree for work: every node knows which node it was born from

A read-only MCP server that lets AI agents search and retrieve the Wheel of Heaven corpus, including source-grounded facts, interpretations, and comparative traditions, all with full epistemic metadata.

Zero-trust local MCP bridge that exposes read-only database tools and structured book navigation to AI agents, with two-tier PII redaction and MPS audit integration.

Enables context capture and reinforcement learning by recording successful work patterns and creating reasoning chains for cross-conversation continuity. Automatically captures positive feedback through Claude Code hooks to build reusable success patterns.

MCP server for searching and retrieving trading knowledge from a centralized SQLite library of book summaries, strategies, and document chunks.

Transforms Claude into a persistent intelligence layer with automatic checkpointing, crash recovery, semantic memory, and cross-project learning. It provides 128+ tools for session management, adversarial testing, and browser automation to eliminate state loss and context rebuild costs.

Provides persistent local memory of Claude Code sessions with TODO management and searchable activity logs via MCP tools.

Provides session persistence, crash recovery, decision tracking, and context compression for AI assistants, enabling seamless multi-session continuity.

Reduces token consumption by over 80% through intelligent file caching, returning only diffs for modified files and suppressing unchanged content. It features a suite of 12 tools for semantic search, batch reading, and efficient file editing to optimize LLM interactions with large codebases.

Exposes any project wiki to AI assistants with features like fuzzy search, content search, lazy loading, and auto-reload.

Enables management of Amazon Bedrock Knowledge Bases including creation, data source configuration, document ingestion, and RAG (Retrieval-Augmented Generation) queries with support for multiple embedding models and custom parsing/chunking strategies.

Provides AI chat history compression tools through token-based trimming and AI-powered summarization strategies to manage conversation context within token limits.

Provides LLMs with cognitive tools (Thinking Models), memory structures (Cells), and multi-agent patterns (Organs) for advanced reasoning, memory management, and orchestration. Automatically routes requests to appropriate strategies and generates custom agent blueprints.

An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.