"Understanding or Using Memory Lists" matching MCP servers:

A simple MCP server implementation in TypeScript that communicates over stdio, allowing users to ask questions that end with 'yes or no' to trigger the MCP tool in Cursor.

MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution

A lightweight TypeScript-based MCP server that demonstrates how to build custom MCP tools by implementing a simple addition calculator. Serves as a starting point for building MCP-compatible tools.

Enables AI assistants to run code in 24 programming languages on HackerEarth's sandboxed servers and read the printed output or errors, without installing local toolchains. It also lists supported languages with exact identifiers for use when submitting code.

Sandboxed bash execution MCP server for AI agents, using an in-memory virtual filesystem overlay to prevent real filesystem damage, with configurable network access, timeouts, and optional Python/JS runtimes.

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.

An MCP server implementing AI-user confirmation protocols, providing tools for LLMs to seek user confirmation when uncertain through yes/no questions, action confirmations, intent clarification, understanding verification, and satisfaction ratings.

Enables AI agents to debug embedded systems by providing a comprehensive interface for GDB operations across multiple architectures like ARM and x86. It supports remote debugging via gdbserver or QEMU, allowing for detailed inspection of memory, registers, stack frames, and variables.

Enables AI agents to code and debug Commodore PET software using the VICE emulator, with CLI and MCP tools for session control, screen reading, memory manipulation, and testing.

MCP server for agentic interaction with the Lean theorem prover via LSP, providing tools for understanding, analyzing, and interacting with Lean projects.

A Model Context Protocol (MCP) server for coordinating work between multiple AI agents in monorepos and shared codebases using a simple file-based signaling system or Discord thread chat.

An MCP server that enables AI agents to debug .NET applications using netcoredbg. It supports core debugging tasks like setting breakpoints, stepping through code, and inspecting variables or stack traces.

Enables AI-powered creation, configuration, and management of DevContainer environments using natural language prompts. Supports 11+ templates for popular development stacks and provides container lifecycle operations through VS Code, Cursor, or any MCP-compatible editor.

An MCP server that enables AI clients to delegate tasks to autonomous developer teams using Goose CLI subagents, supporting parallel or sequential execution of specialized agents for different development roles.

Enables AI assistants to solve linear, integer, mixed-integer, and knapsack optimization problems using Google OR-Tools via a simple JSON interface.

This server enables natural language interaction between a user and their Kuzu databases using clients like Claude Desktop or Cursor, allowing LLMs to retrieve the database schema, execute Cypher queries, create nodes, and establish relationships in the graph database.

An MCP server that executes tox commands to run Python tests within a project using pytest, allowing users to run all tests or specific test groups, files, cases, or directories.

Provides a secure, isolated JavaScript execution environment with configurable time and memory limits for safely running code from Claude.

A simple template for creating custom tools for Cursor IDE using Model Context Protocol, deployable via Heroku, Docker, or directly within Cursor IDE.

Provides AI assistants with deep semantic and runtime understanding of Jupyter notebooks.