"Improving OKR Strategies" matching MCP servers:

An MCP server that exposes RAG retrieval evaluation as agent tools, allowing agents to retrieve passages and measure retrieval quality across multiple strategies.

Enables searching documents in a Tropicalia project using hybrid, neural, or keyword strategies, with optional AI-generated answers and source inclusion.

A Model Context Protocol server providing real-time stock data and options analysis through Yahoo Finance, enabling LLMs to access market data, analyze stocks, and evaluate options strategies.

Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs

Tools to benchmark chunking strategies for your RAG corpus.

Provides a 'think' tool that allows Claude and other LLMs to add dedicated thinking steps during complex tool use scenarios, creating space for structured reasoning and improving problem-solving capabilities.

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.

Enables AI assistants to search and query documentation with flexible backend configurations (ChromaDB, Xenova embeddings) and supports multiple chunking strategies via an MCP server.

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.

SkillMesh is a retrieval router for MCP/LLM tool catalogs. It selects top-K relevant expert cards instead of loading every tool, reducing prompt size, by improving tool selection, and lowering token cost. Supports Claude MCP, Codex skills, and OpenAI-style function schemas.

Enables LLM agents to query a codebase's structural knowledge (symbols, imports, call graphs, etc.) via MCP, reducing tokens and improving correctness compared to raw file access.

ktx is an MCP server that provides agents with accurate warehouse querying by serving a self-improving context layer of approved metrics, joinable columns, and business knowledge through CLI and MCP tools.

Provides access to Daniel Miessler's Fabric AI prompts (patterns and strategies) through MCP, automatically syncing with the upstream repository to enable powerful prompt templates in AI workflows.

Provides persistent local memory functionality for AI assistants, enabling them to store, retrieve, and search contextual information across conversations with SQLite-based full-text search. All data stays private on your machine while dramatically improving context retention and personalized assistance.

A knowledge graph MCP server that integrates Graphiti and the ACE framework for conversational management of Neo4j-based entities and relationships. It enables AI agents to perform semantic searches, manage data isolation, and utilize automatic learning strategies.

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.

MCP Long Context Reader is a Python-based toolkit designed to overcome the context window limitations and high costs associated with Large Language Models (LLMs) processing extensive documents. It provides a FastMCP server with multiple, powerful strategies for an LLM agent to 'read' and query long documents without needing to load the entire text into its context window.