MCP CLI - Model Context Protocol Command Line Interface

What is MCP CLI - Model Context Protocol Command Line Interface?

MCP CLI is a command-line interface for interacting with Model Context Protocol (MCP) servers. Built on the CHUK-MCP pure Python protocol library, it provides multiple operational modes for LLM communication, tool usage, and conversation management.

How to use MCP CLI - Model Context Protocol Command Line Interface?

Install from source by cloning the repository and running pip install -e ".[cli,dev]". Configure servers in server_config.json and set API keys as environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, or a local Ollama installation). Use global arguments like --server, --provider, --model, and --config-file. If you encounter a "Missing argument 'KWARGS'" error, use the equals sign format (e.g., --server=sqlite) or add a double-dash (--) before arguments.

Key features of MCP CLI - Model Context Protocol Command Line Interface

• Multiple operational modes: Chat, Interactive, Command, and Direct Commands
• Multi-provider support: OpenAI, Anthropic, and Ollama integrations
• Robust tool system with automatic discovery, execution, and history tracking
• Advanced conversation management with filtering, JSON export, and compaction
• Rich user experience with command completion, colored output, and progress indicators
• Resilient resource management with graceful error handling and cleanup

Use cases of MCP CLI - Model Context Protocol Command Line Interface

• Conversational LLM interaction with automatic tool usage in Chat mode
• Scriptable automation and pipeline integration using Command mode
• Direct server operations through Interactive mode's command-driven shell
• Batch processing of multiple files with GNU Parallel and Command mode

FAQ from MCP CLI - Model Context Protocol Command Line Interface

What are the prerequisites for installing MCP CLI?

Python 3.11 or higher is required, along with valid API keys for OpenAI (OPENAI_API_KEY) or Anthropic (ANTHROPIC_API_KEY), or a local Ollama installation. A server configuration file (server_config.json by default) is also needed.

Which LLM providers and models are supported?

OpenAI (e.g., gpt-4o-mini, gpt-4o, gpt-4-turbo), Ollama (e.g., llama3.2, qwen2.5-coder), and Anthropic (e.g., claude-3-opus, claude-3-sonnet) are supported. The architecture is extensible for additional providers.

How do I resolve the "Missing argument 'KWARGS'" error?

Use the equals sign format for all arguments (e.g., mcp-cli chat --server=sqlite --provider=ollama) or add a double-dash (--) before the arguments (e.g., mcp-cli chat -- --server sqlite). When using uv with multiple parameters, append an empty string at the end.

What is the difference between Chat mode and Interactive mode?

Chat mode provides a natural language conversational interface where the LLM can automatically use available tools. Interactive mode offers a command-driven shell interface for direct server operations (e.g., listing tools, calling resources, managing providers).

Can I use MCP CLI in scripts or pipelines?

Yes, Command mode (mcp-cli cmd) is designed for Unix-friendly automation. It supports input/output file paths, prompt templates, direct tool calls, and piping stdin/stdout, making it suitable for batch processing and integration into shell scripts.