"Optimizing AI Model Thinking, Token Usage, and Context Size" matching MCP servers:

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

Provides sandboxed code execution and data processing for CSVs and logs to achieve over 95% token savings. It enables secure multi-language execution and progressive tool disclosure to optimize LLM context usage.

An MCP server that keeps raw tool output out of the context window by sandboxing and compressing results (about 98% reduction), persisting file edits, git operations, tasks and decisions in SQLite with BM25 retrieval so the agent resumes after compaction, and enforcing routing across 17 platforms via MCP and hooks. It also encourages the model to write scripts that compute answers and log only results instead of reading large data into context.

Context Mode is an MCP server that reduces context window waste by sandboxing data-heavy tools, tracking session state in SQLite, and promoting code-based analysis over raw data reads, achieving up to 98% context savings.

An MCP server that preserves LLM context by intercepting large data outputs and returning only concise summaries or relevant sections. It enables efficient sandboxed code execution, file processing, and documentation indexing across multiple programming languages and authenticated CLIs.

An MCP server that enables processing of massive datasets up to 10M+ tokens using a recursive language model pattern for strategic chunking and analysis. It automates sub-queries and result aggregation using free local inference via Ollama or the Claude API to handle context beyond standard prompt limits.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.

IMCP - Insecure Model Context Protocol The DVWA for AI Security! Welcome to IMCP – a deliberately vulnerable framework that exposes 16 critical security weaknesses in AI/ML systems. Whether you're a security researcher, developer, or educator, IMCP is your playground for hands-on learning about real

MCP server to provide Sketch layout information to AI coding agents like Cursor

An MCP server for integrating AI models with client applications, allowing for structured interactions with APIs using command-line arguments for authentication and configuration.

A TypeScript wrapper library for the Model Context Protocol SDK that provides a simplified interface for creating MCP servers with tools, resources, and prompts without needing to work directly with the protocol.

A GitHub-based implementation of the Model Context Protocol that enables LLMs to access and manipulate data in a Universal Object Reference (UOR) framework, providing standardized interfaces for object management with decentralized storage.

A server implementation demonstrating how AI models can interact with external tools and services through Model Context Protocol, featuring integrations for calculator functions, GitHub repositories, and Google Maps searches.

Enables AI agents to program MicroPython boards such as Raspberry Pi Pico and ESP32 through Thonny, with visible shell commands and optional human approval.

An MCP server that queries multiple Ollama models and combines their responses, providing diverse AI perspectives on a single question for more comprehensive answers.

A Model Context Protocol server that enables LLMs to read, search, and analyze code files with advanced caching and real-time file watching capabilities.

An MCP server that enhances AI agents' coding capabilities by providing zero hallucinations, improved code quality, security-first approach, high test coverage, and efficient context management.

Stop stitching context for Copilot and Cursor. With Sequa MCP, your AI tools know your entire codebase and docs out of the box.

Allows users to integrate their custom Quickchat AI Agents into various AI applications (Claude Desktop, Cursor, VS Code, etc.) through the Model Context Protocol, enabling AI-to-AI interactions.

MCP server for secure, session-based Python code execution in Docker containers, enabling LLM applications to run code, manage state, and access files.

Enables AI-native statistical analysis and reproducible research workflows through MCP, including natural language planning, protocol-based analysis, Python/R cross-validation, and publication-ready figure generation.

This server lets tools-enabled LLMs work directly with SWI‑Prolog. It supports loading Prolog files, adding/removing facts and rules, listing symbols, and running queries with two modes: deterministic pagination and true engine backtracking.

A Model Context Protocol (MCP) server that supports STDIO, SSE and Streamable HTTP protocols for AI model interactions.

MCP server for portable context management across AI assistants, providing tools to store and retrieve persistent context, instructions, and execute sandboxed bash commands with automatic git commits, using OAuth 2.1 and magic link authentication.

Context-Pods is a comprehensive development framework for creating, testing, and managing Model Context Protocol (MCP) servers. It provides a Meta-MCP Server that can generate other MCP servers through natural language descriptions or by wrapping existing scripts.

All In One Model Context Protocol: qdrant, google suite (gmail, calendar, ...), jira, gitlab, CLI, ...

An educational project that deliberately implements vulnerable MCP servers to demonstrate various security risks like prompt injection, tool poisoning, and code execution for training security researchers and AI safety professionals.

A lightweight Python-based server designed to run, manage and create CrewAI workflows using the Model Context Protocol for communicating with LLMs and tools like Claude Desktop or Cursor IDE.

Remote-control Unity Editor from any AI IDE via file-based IPC — 62 tools, zero dependencies