MCP CLI - Model Context Protocol Command Line Interface | MCP Client

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MCP CLI - Model Context Protocol Command Line Interface | MCP Client

MCP CLI - Model Context Protocol Command Line Interface

A powerful, feature-rich command-line interface for interacting with Model Context Protocol servers. This client enables seamless communication with LLMs through integration with the CHUK-MCP protocol library which is a pyodide compatible pure python protocol implementation of MCP, supporting tool usage, conversation management, and multiple operational modes.

🔄 Protocol Implementation

The core protocol implementation has been moved to a separate package at:
https://github.com/chrishayuk/chuk-mcp

This CLI is built on top of the protocol library, focusing on providing a rich user experience while the protocol library handles the communication layer.

🌟 Features

• Multiple Operational Modes:

  • Chat Mode: Conversational interface with direct LLM interaction and automated tool usage
  • Interactive Mode: Command-driven interface for direct server operations
  • Command Mode: Unix-friendly mode for scriptable automation and pipelines
  • Direct Commands: Run individual commands without entering interactive mode

• Multi-Provider Support:

  • OpenAI integration (gpt-4o-mini, gpt-4o, gpt-4-turbo, etc.)
  • Ollama integration (llama3.2, qwen2.5-coder, etc.)
  • Anthropic integration (claude-3-opus, claude-3-sonnet, etc.)
  • Extensible architecture for additional providers

• Provider and Model Management:

  • Configure multiple LLM providers (API keys, endpoints, default models)
  • Switch between providers and models during sessions
  • Command-line arguments for provider/model selection
  • Interactive commands for provider configuration

• Robust Tool System:

  • Automatic discovery of server-provided tools
  • Server-aware tool execution
  • Tool call history tracking and analysis
  • Support for complex, multi-step tool chains

• Advanced Conversation Management:

  • Complete conversation history tracking
  • Filtering and viewing specific message ranges
  • JSON export capabilities for debugging or analysis
  • Conversation compaction for reduced token usage

• Rich User Experience:

  • Command completion with context-aware suggestions
  • Colorful, formatted console output
  • Progress indicators for long-running operations
  • Detailed help and documentation

• Resilient Resource Management:

  • Proper cleanup of asyncio resources
  • Graceful error handling
  • Clean terminal restoration
  • Support for multiple simultaneous server connections

📋 Prerequisites

• Python 3.11 or higher
• For OpenAI: Valid API key in OPENAI_API_KEY environment variable
• For Anthropic: Valid API key in ANTHROPIC_API_KEY environment variable
• For Ollama: Local Ollama installation
• Server configuration file (default: server_config.json)
• CHUK-MCP protocol library

🚀 Installation

Install from Source

1. Clone the repository:

git clone https://github.com/chrishayuk/mcp-cli
cd mcp-cli  

2. Install the package with development dependencies:

pip install -e ".[cli,dev]"

3. Run the CLI:

mcp-cli --help

Using UV (Alternative Installation)

If you prefer using UV for dependency management:

# Install UV if not already installed
pip install uv

# Install dependencies
uv sync --reinstall

# Run using UV
uv run mcp-cli --help

🧰 Global Command-line Arguments

Global options available for all modes and commands:

• --server: Specify the server(s) to connect to (comma-separated for multiple)
• --config-file: Path to server configuration file (default: server_config.json)
• --provider: LLM provider to use (openai, anthropic, ollama, default: openai)
• --model: Specific model to use (provider-dependent defaults)
• --disable-filesystem: Disable filesystem access (default: true)

CLI Argument Format Issue

You might encounter a "Missing argument 'KWARGS'" error when running various commands. This is due to how the CLI parser is configured. To resolve this, use one of these approaches:

1. Use the equals sign format for all arguments:

   mcp-cli tools call --server=sqlite
   mcp-cli chat --server=sqlite --provider=ollama --model=llama3.2

2. Add a double-dash (--) after the command and before arguments:

   mcp-cli tools call -- --server sqlite
   mcp-cli chat -- --server sqlite --provider ollama --model llama3.2

3. When using uv and multiple extra parameters, follow the 2nd step but add an empty string at the end:

   uv run mcp-cli chat -- --server sqlite --provider ollama --model llama3.2 ""
   

These format issues apply to all commands (chat, interactive, tools, etc.) and are due to how the argument parser interprets positional vs. named arguments.

🌐 Available Modes

1. Chat Mode

Chat mode provides a natural language interface for interacting with LLMs, where the model can automatically use available tools:

# Default (makes chat the default when no other command is specified)
uv run mcp-cli

# Explicit chat mode
uv run mcp-cli chat --server sqlite

# With specific provider and model
uv run mcp-cli chat --server sqlite --provider openai --model gpt-4o

2. Interactive Mode

Interactive mode provides a command-driven shell interface for direct server operations:

uv run mcp-cli interactive --server sqlite

3. Command Mode (Cmd)

Command mode provides a Unix-friendly interface for automation and pipeline integration:

uv run mcp-cli cmd --server sqlite [options]

4. Direct Commands

Run individual commands without entering an interactive mode:

# List available tools
uv run mcp-cli tools list {} --server sqlite

# Call a specific tool
uv run mcp-cli tools call {} --server sqlite

🤖 Using Chat Mode

Chat mode provides a conversational interface with the LLM, automatically using available tools when needed.

Starting Chat Mode

# Default with {} for KWARGS
uv run mcp-cli --server sqlite

# Explicit chat mode with {}
uv run mcp-cli chat --server sqlite

# With specific provider and model
uv run mcp-cli chat --server sqlite --provider openai --model gpt-4o

# Note: Be careful with the command syntax
# Correct format without any KWARGS parameter
uv run mcp-cli chat --server sqlite --provider ollama --model llama3.2

# Or if you encounter the "Missing argument 'KWARGS'" error, try:
uv run mcp-cli chat --server=sqlite --provider=ollama --model=llama3.2

Chat Commands

In chat mode, use these slash commands:

General Commands

• /help: Show available commands
• /help <command>: Show detailed help for a specific command
• /quickhelp or /qh: Display a quick reference of common commands
• exit or quit: Exit chat mode

Provider and Model Commands

• /provider or /p: Display or manage LLM providers
  • /provider: Show current provider and model
  • /provider list: List all configured providers
  • /provider config: Show detailed provider configuration
  • /provider set <name> <key> <value>: Set a provider configuration value
  • /provider <name>: Switch to a different provider
• /model or /m: Display or change the current model
  • /model: Show current model
  • /model <name>: Switch to a different model

Tool Commands

• /tools: Display all available tools with their server information
  • /tools --all: Show detailed tool information including parameters
  • /tools --raw: Show raw tool definitions
• /toolhistory or /th: Show history of tool calls in the current session
  • /th <N>: Show details for a specific tool call
  • /th -n 5: Show only the last 5 tool calls
  • /th --json: Show tool calls in JSON format

Conversation Commands

• /conversation or /ch: Show the conversation history
  • /ch <N>: Show a specific message from history
  • /ch -n 5: Show only the last 5 messages
  • /ch <N> --json: Show a specific message in JSON format
  • /ch --json: View the entire conversation history in raw JSON format
• /save <filename>: Save conversation history to a JSON file
• /compact: Condense conversation history into a summary

Display Commands

• /cls: Clear the screen while keeping conversation history
• /clear: Clear both the screen and conversation history
• /verbose or /v: Toggle between verbose and compact tool display modes

Control Commands

• /interrupt, /stop, or /cancel: Interrupt running tool execution
• /servers: List connected servers and their status

🖥️ Using Interactive Mode

Interactive mode provides a command-driven shell interface for direct server interaction.

Starting Interactive Mode

# Using {} to satisfy KWARGS requirement
mcp-cli interactive {} --server sqlite

Interactive Commands

In interactive mode, use these commands:

• help: Show available commands
• exit or quit or q: Exit interactive mode
• clear or cls: Clear the terminal screen
• servers or srv: List connected servers with their status
• provider or p: Manage LLM providers
  • provider: Show current provider and model
  • provider list: List all configured providers
  • provider config: Show detailed provider configuration
  • provider set <name> <key> <value>: Set a provider configuration value
  • provider <name>: Switch to a different provider
• model or m: Display or change the current model
  • model: Show current model
  • model <name>: Switch to a different model
• tools or t: List available tools or call one interactively
  • tools --all: Show detailed tool information
  • tools --raw: Show raw JSON definitions
  • tools call: Launch the interactive tool-call UI
• resources or res: List available resources from all servers
• prompts or p: List available prompts from all servers
• ping: Ping connected servers (optionally filter by index/name)

📄 Using Command Mode (Cmd)

Command mode provides a Unix-friendly interface for automation and pipeline integration.

Starting Command Mode

# Using {} to satisfy KWARGS requirement
mcp-cli cmd {} --server sqlite [options]

Command Mode Options

• --input: Input file path (use - for stdin)
• --output: Output file path (use - for stdout, default)
• --prompt: Prompt template (use {{input}} as placeholder for input)
• --raw: Output raw text without formatting
• --tool: Directly call a specific tool
• --tool-args: JSON arguments for tool call
• --system-prompt: Custom system prompt
• --verbose: Enable verbose logging
• --provider: Specify LLM provider
• --model: Specify model to use

Command Mode Examples

Process content with LLM:

# Summarize a document (with {} for KWARGS)
uv run mcp-cli cmd --server sqlite --input document.md --prompt "Summarize this: {{input}}" --output summary.md

# Process stdin and output to stdout
cat document.md | mcp-cli cmd {} --server sqlite --input - --prompt "Extract key points: {{input}}"

# Use a specific provider and model
uv run mcp-cli cmd {} --server sqlite --input document.md --prompt "Summarize: {{input}}" --provider anthropic --model claude-3-opus

Call tools directly:

# List database tables
uv run mcp-cli cmd {} --server sqlite --tool list_tables --raw

# Run a SQL query
uv run mcp-cli cmd {} --server sqlite --tool read_query --tool-args '{"query": "SELECT COUNT(*) FROM users"}'

Batch processing:

# Process multiple files with GNU Parallel
ls *.md | parallel mcp-cli cmd --server sqlite --input {} --output {}.summary.md --prompt "Summarize: {{input}}"

🔧 Direct CLI Commands

Run individual commands without entering interactive mode:

Provider Commands

# Show current provider configuration
mcp-cli provider show

# List all configured providers
mcp-cli provider list

# Show detailed provider configuration
mcp-cli provider config

# Set a configuration value
mcp-cli provider set <provider_name> <key> <value>
# Example: mcp-cli provider set openai api_key "sk-..."

Tools Commands

# List all tools (using {} to satisfy KWARGS requirement)
uv run mcp-cli tools list {} --server sqlite

# Show detailed tool information
uv run mcp-cli tools list {} --server sqlite --all

# Show raw tool definitions
uv run mcp-cli tools list {} --server sqlite --raw

# Call a specific tool interactively
uv run mcp-cli tools call {} --server sqlite

Resources and Prompts Commands

# List available resources
uv run mcp-cli resources list {} --server sqlite

# List available prompts
uv run mcp-cli prompts list {} --server sqlite

Server Commands

# Ping all servers
uv run mcp-cli ping {} --server sqlite

# Ping specific server(s)
uv run mcp-cli ping {} --server sqlite,another-server

📂 Server Configuration

Create a server_config.json file with your server configurations:

{
  "mcpServers": {
    "sqlite": {
      "command": "python",
      "args": ["-m", "mcp_server.sqlite_server"],
      "env": {
        "DATABASE_PATH": "your_database.db"
      }
    },
    "another-server": {
      "command": "python",
      "args": ["-m", "another_server_module"],
      "env": {}
    }
  }
}

🔐 Provider Configuration

Provider configurations are stored with these key settings:

• api_key: API key for authentication
• api_base: Base URL for API requests
• default_model: Default model to use with this provider
• Other provider-specific settings

Environment Variables

You can also set the default provider and model using environment variables:

export LLM_PROVIDER=openai
export LLM_MODEL=gpt-4o-mini

Configuration Example

The provider configuration is typically stored in a JSON file and looks like:

{
  "openai": {
    "api_key": "sk-...",
    "api_base": "https://api.openai.com/v1",
    "default_model": "gpt-4o-mini"
  },
  "anthropic": {
    "api_key": "sk-...",
    "api_base": "https://api.anthropic.com",
    "default_model": "claude-3-opus"
  },
  "ollama": {
    "api_base": "http://localhost:11434",
    "default_model": "llama3.2"
  }
}

📈 Advanced Usage Examples

Provider and Model Selection

You can change providers or models during a session:

# In chat mode
> /provider
Current provider: openai
Current model: gpt-4o-mini
To change provider: /provider <provider_name>

> /provider list
Available Providers
┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ Provider  ┃ Default Model  ┃ API Base                        ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ openai    │ gpt-4o-mini    │ https://api.openai.com/v1       │
│ anthropic │ claude-3-opus  │ https://api.anthropic.com       │
│ ollama    │ llama3.2       │ http://localhost:11434          │
└───────────┴────────────────┴─────────────────────────────────┘

> /provider anthropic
Switched to provider: anthropic with model: claude-3-opus
LLM client updated successfully

> /model claude-3-sonnet
Switched to model: claude-3-sonnet

Working with Tools in Chat Mode

In chat mode, simply ask questions that require tool usage, and the LLM will automatically call the appropriate tools:

You: What tables are available in the database?
[Tool Call: list_tables]
Assistant: There's one table in the database named products. How would you like to proceed?

You: Select top 10 products ordered by price in descending order
[Tool Call: read_query]
Assistant: Here are the top 10 products ordered by price in descending order:
  1 Mini Drone - $299.99
  2 Smart Watch - $199.99
  3 Portable SSD - $179.99
  ...

Using Conversation Management

The MCP CLI provides powerful conversation history management:

> /conversation
Conversation History (12 messages)
# | Role      | Content
1 | system    | You are an intelligent assistant capable of using t...
2 | user      | What tables are available in the database?
3 | assistant | Let me check for you.
...

> /save conversation.json
Conversation saved to conversation.json

> /compact
Conversation history compacted with summary.

🛠️ Implementation Details

The provider configuration is managed by the ProviderConfig class, which:

• Loads/saves configuration from a local file
• Manages active provider and model settings
• Provides helper methods for retrieving configuration values

The LLM client is created using the get_llm_client function, which instantiates the appropriate client based on the provider and model settings.

📦 Dependencies

The CLI is organized with optional dependency groups:

• cli: Rich terminal UI, command completion, and provider integrations
• dev: Development tools and testing utilities
• wasm: (Reserved for future WebAssembly support)
• chuk-mcp: Protocol implementation library (core dependency)

Install with specific extras using:

pip install "mcp-cli[cli]"     # Basic CLI features
pip install "mcp-cli[cli,dev]" # CLI with development tools

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Rich for beautiful terminal formatting
• Typer for CLI argument parsing
• Prompt Toolkit for interactive input
• CHUK-MCP for the core protocol implementation

https://mcp.so/clients/mcp-cli

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