"Improving OKR 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.

Self-improving, verifiable memory for AI coding agents. Learns how you work, stops repeating mistakes, models each project, recalls the right lesson at the right moment. Every memory is signed and tamper-evident. Local-first.

A self-improving knowledge agent that provides local retrieval, web research, and structured answer synthesis for Claude Code and VS Code Copilot via MCP. It enables AI tools to manage a project-specific knowledge lifecycle through automated research and indexing.

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.

Self-improving learning engine for Claude Code. Extracts signals from sessions (corrections, successes,confusion), runs periodic reflections, and evolves behavioral frameworks through evidence. 9 MCP tools, automatichooks, local-only.

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.

Multi-agent shared brain MCP server enabling cross-session memory, self-improving skill loops, and inter-agent signaling across Claude, Codex, Gemini, Copilot, and VS Code CLI agents.

ᑭᑫᓐᑖᓱᐎᓐ ᐋᐸᒋᒋᑲᓇᓐ - Gikendaasowin Aabajichiganan - Cognitive Tools MCP server implemented from various prompting strategies.

An MCP server for saving, managing, and enhancing context with pre-processing strategies like clarification, analysis, and search optimization.

An MCP server that provides a self-improving knowledge graph with per-triple provenance and deterministic reasoning, enabling auditable, reproducible, and contradiction-aware answers for AI agents.

Provides agents with cross-session memory by compressing run trajectories into reusable conclusions and strategies and recalling relevant priors via MCP.

Provides Claude with persistent memory and learning capabilities through 10 automatic agents that capture decisions, errors, solutions, and patterns across conversations. Features an anti-compaction system to prevent context loss and enables infinite conversation continuity.

An open-source MCP server for RAG over personal documents. Supports three parallel strategies — Traditional, Contextual, and Graph RAG — with all data stored locally for privacy.

Provides a find_solution tool that retrieves verified, compressed coding solutions from a curated library and injects them into AI coding agents like Claude Code and Cursor via MCP, improving problem-solving success rates by up to 50%.

Provides 78 tools for AGI experiments based on universal physics principles like entropy and criticality. It enables advanced long-term memory management, diversity-focused information retrieval, and meta-cognitive monitoring for self-improving AI systems.

Self-improving documentation engine that ingests Slack and GitHub PRs to detect knowledge gaps and improve documentation automatically.

A local Obsidian Vault authoring MCP for building high-quality knowledge inputs for OKC, enabling note creation, search, and audit without the Obsidian app.

Implements Agentic Context Engineering to create self-improving AI coding assistants that learn from execution feedback and build persistent knowledge playbooks. Reduces token usage by 86.9% while improving code accuracy by 10.6% through incremental context updates.