MCP CLI is a command-line interface designed to streamline interactions with Model Context Protocol (MCP) servers. The latest version, v0.3.0, introduces a new three-subcommand architecture (info, grep, call), connection pooling, and tool filtering. MCP is an open standard that connects AI agents with external tools and APIs, but as more MCP servers emerge, developers face challenges related to context window bloat. Traditional methods require loading all tool definitions upfront, which can use around 47,000 tokens for six servers and 60 tools. In contrast, MCP CLI reduces this to about 400 tokens by implementing dynamic context discovery. With dynamic context discovery, agents request only the information they need at the moment. For instance, instead of loading all tool definitions at once, an agent will first ask which servers are available, then check the parameters for a specific tool, and finally execute the tool. This method significantly cuts down on token consumption, allowing for more effective use of context, minimizing interruptions during workflows, and reducing API costs. The CLI supports both local and remote servers and uses lazy-spawn connection pooling to avoid delays from server startup. Each server runs its own daemon, with a configurable idle timeout. MCP CLI also includes a filtering feature that lets users define which tools are available or excluded based on specific patterns, ensuring that only relevant tools are loaded for a session. This filtering applies globally across all operations, enhancing efficiency. For integration with AI agents, users can either add system instructions or utilize a skill definition that comes with the CLI. The project is open source and aims to fit seamlessly into existing workflows, allowing developers and AI agents to leverage a vast ecosystem of tools without the baggage of static configurations.