base on # 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 Tool Processor](https://github.com/chrishayuk/chuk-tool-processor) and [CHUK-LLM](https://github.com/chrishayuk/chuk-llm), providing tool usage, conversation management, and multiple operational modes. **Default Configuration**: MCP CLI defaults to using Ollama with the `gpt-oss` reasoning model for local, privacy-focused operation without requiring API keys. ## 🔄 Architecture Overview The MCP CLI is built on a modular architecture with clean separation of concerns: - **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)**: Async-native tool execution and MCP server communication - **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)**: Unified LLM provider configuration and client management with 200+ auto-generated functions - **[CHUK-Term](https://github.com/chrishayuk/chuk-term)**: Enhanced terminal UI with themes, cross-platform terminal management, and rich formatting - **MCP CLI**: Command orchestration and integration layer (this project) ## 🌟 Features ### Multiple Operational Modes - **Chat Mode**: Conversational interface with streaming responses and automated tool usage (default: Ollama/gpt-oss) - **Interactive Mode**: Command-driven shell interface for direct server operations - **Command Mode**: Unix-friendly mode for scriptable automation and pipelines - **Direct Commands**: Run individual commands without entering interactive mode ### Advanced Chat Interface - **Streaming Responses**: Real-time response generation with live UI updates - **Reasoning Visibility**: See AI's thinking process with reasoning models (gpt-oss, GPT-5, Claude 4) - **Concurrent Tool Execution**: Execute multiple tools simultaneously while preserving conversation order - **Smart Interruption**: Interrupt streaming responses or tool execution with Ctrl+C - **Performance Metrics**: Response timing, words/second, and execution statistics - **Rich Formatting**: Markdown rendering, syntax highlighting, and progress indicators ### Comprehensive Provider Support MCP CLI supports all providers and models from CHUK-LLM, including cutting-edge reasoning models: | Provider | Key Models | Special Features | |----------|------------|------------------| | **Ollama** (Default) | 🧠 gpt-oss, llama3.3, llama3.2, qwen3, qwen2.5-coder, deepseek-coder, granite3.3, mistral, gemma3, phi3, codellama | Local reasoning models, privacy-focused, no API key required | | **OpenAI** | 🚀 GPT-5 family (gpt-5, gpt-5-mini, gpt-5-nano), GPT-4o family, O3 series (o3, o3-mini) | Advanced reasoning, function calling, vision | | **Anthropic** | 🧠 Claude 4 family (claude-4-1-opus, claude-4-sonnet), Claude 3.5 Sonnet | Enhanced reasoning, long context | | **Azure OpenAI** 🏢 | Enterprise GPT-5, GPT-4 models | Private endpoints, compliance, audit logs | | **Google Gemini** | Gemini 2.0 Flash, Gemini 1.5 Pro | Multimodal, fast inference | | **Groq** ⚡ | Llama 3.1 models, Mixtral | Ultra-fast inference (500+ tokens/sec) | | **Perplexity** 🌐 | Sonar models | Real-time web search with citations | | **IBM watsonx** 🏢 | Granite, Llama models | Enterprise compliance | | **Mistral AI** 🇪🇺 | Mistral Large, Medium | European, efficient models | ### Robust Tool System - **Automatic Discovery**: Server-provided tools are automatically detected and catalogued - **Provider Adaptation**: Tool names are automatically sanitized for provider compatibility - **Concurrent Execution**: Multiple tools can run simultaneously with proper coordination - **Rich Progress Display**: Real-time progress indicators and execution timing - **Tool History**: Complete audit trail of all tool executions - **Streaming Tool Calls**: Support for tools that return streaming data ### Advanced Configuration Management - **Environment Integration**: API keys and settings via environment variables - **File-based Config**: YAML and JSON configuration files - **User Preferences**: Persistent settings for active providers and models - **Validation & Diagnostics**: Built-in provider health checks and configuration validation ### Enhanced User Experience - **Cross-Platform Support**: Windows, macOS, and Linux with platform-specific optimizations via chuk-term - **Rich Console Output**: Powered by chuk-term with 8 built-in themes (default, dark, light, minimal, terminal, monokai, dracula, solarized) - **Advanced Terminal Management**: Cross-platform terminal operations including clearing, resizing, color detection, and cursor control - **Interactive UI Components**: User input handling through chuk-term's prompt system (ask, confirm, select_from_list, select_multiple) - **Command Completion**: Context-aware tab completion for all interfaces - **Comprehensive Help**: Detailed help system with examples and usage patterns - **Graceful Error Handling**: User-friendly error messages with troubleshooting hints ## 📋 Prerequisites - **Python 3.11 or higher** - **For Local Operation (Default)**: - Ollama: Install from [ollama.ai](https://ollama.ai) - Pull the default reasoning model: `ollama pull gpt-oss` - **For Cloud Providers** (Optional): - OpenAI: `OPENAI_API_KEY` environment variable (for GPT-5, GPT-4, O3 models) - Anthropic: `ANTHROPIC_API_KEY` environment variable (for Claude 4, Claude 3.5) - Azure: `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` (for enterprise GPT-5) - Google: `GEMINI_API_KEY` (for Gemini models) - Groq: `GROQ_API_KEY` (for fast Llama models) - Custom providers: Provider-specific configuration - **MCP Servers**: Server configuration file (default: `server_config.json`) ## 🚀 Installation ### Quick Start with Ollama (Default) 1. **Install Ollama** (if not already installed): ```bash # macOS/Linux curl -fsSL https://ollama.ai/install.sh | sh # Or visit https://ollama.ai for other installation methods ``` 2. **Pull the default reasoning model**: ```bash ollama pull gpt-oss # Open-source reasoning model with thinking visibility ``` 3. **Install and run MCP CLI**: ```bash # Using uvx (recommended) uvx mcp-cli --help # Or install from source git clone https://github.com/chrishayuk/mcp-cli cd mcp-cli pip install -e "." mcp-cli --help ``` ### Using Different Models ```bash # === LOCAL MODELS (No API Key Required) === # Use default reasoning model (gpt-oss) mcp-cli --server sqlite # Use other Ollama models mcp-cli --model llama3.3 # Latest Llama mcp-cli --model qwen2.5-coder # Coding-focused mcp-cli --model deepseek-coder # Another coding model mcp-cli --model granite3.3 # IBM Granite # === CLOUD PROVIDERS (API Keys Required) === # GPT-5 Family (requires OpenAI API key) mcp-cli --provider openai --model gpt-5 # Full GPT-5 with reasoning mcp-cli --provider openai --model gpt-5-mini # Efficient GPT-5 variant mcp-cli --provider openai --model gpt-5-nano # Ultra-lightweight GPT-5 # GPT-4 Family mcp-cli --provider openai --model gpt-4o # GPT-4 Optimized mcp-cli --provider openai --model gpt-4o-mini # Smaller GPT-4 # O3 Reasoning Models mcp-cli --provider openai --model o3 # O3 reasoning mcp-cli --provider openai --model o3-mini # Efficient O3 # Claude 4 Family (requires Anthropic API key) mcp-cli --provider anthropic --model claude-4-1-opus # Most advanced Claude mcp-cli --provider anthropic --model claude-4-sonnet # Balanced Claude 4 mcp-cli --provider anthropic --model claude-3-5-sonnet # Claude 3.5 # Enterprise Azure (requires Azure configuration) mcp-cli --provider azure_openai --model gpt-5 # Enterprise GPT-5 # Other Providers mcp-cli --provider gemini --model gemini-2.0-flash # Google Gemini mcp-cli --provider groq --model llama-3.1-70b # Fast Llama via Groq ``` ## 🧰 Global Configuration ### Default Configuration MCP CLI defaults to: - **Provider**: `ollama` (local, no API key required) - **Model**: `gpt-oss` (open-source reasoning model with thinking visibility) ### Command-line Arguments Global options available for all modes and commands: - `--server`: Specify server(s) to connect to (comma-separated) - `--config-file`: Path to server configuration file (default: `server_config.json`) - `--provider`: LLM provider (default: `ollama`) - `--model`: Specific model to use (default: `gpt-oss` for Ollama) - `--disable-filesystem`: Disable filesystem access (default: enabled) - `--api-base`: Override API endpoint URL - `--api-key`: Override API key (not needed for Ollama) - `--verbose`: Enable detailed logging - `--quiet`: Suppress non-essential output ### Environment Variables ```bash # Override defaults export LLM_PROVIDER=ollama # Default provider (already the default) export LLM_MODEL=gpt-oss # Default model (already the default) # For cloud providers (optional) export OPENAI_API_KEY=sk-... # For GPT-5, GPT-4, O3 models export ANTHROPIC_API_KEY=sk-ant-... # For Claude 4, Claude 3.5 export AZURE_OPENAI_API_KEY=sk-... # For enterprise GPT-5 export AZURE_OPENAI_ENDPOINT=https://... export GEMINI_API_KEY=... # For Gemini models export GROQ_API_KEY=... # For Groq fast inference # Tool configuration export MCP_TOOL_TIMEOUT=120 # Tool execution timeout (seconds) ``` ## 🌐 Available Modes ### 1. Chat Mode (Default) Provides a natural language interface with streaming responses and automatic tool usage: ```bash # Default mode with Ollama/gpt-oss reasoning model (no API key needed) mcp-cli --server sqlite # See the AI's thinking process with reasoning models mcp-cli --server sqlite --model gpt-oss # Open-source reasoning mcp-cli --server sqlite --provider openai --model gpt-5 # GPT-5 reasoning mcp-cli --server sqlite --provider anthropic --model claude-4-1-opus # Claude 4 reasoning # Use different local models mcp-cli --server sqlite --model llama3.3 mcp-cli --server sqlite --model qwen2.5-coder # Switch to cloud providers (requires API keys) mcp-cli chat --server sqlite --provider openai --model gpt-5 mcp-cli chat --server sqlite --provider anthropic --model claude-4-sonnet ``` ### 2. Interactive Mode Command-driven shell interface for direct server operations: ```bash mcp-cli interactive --server sqlite # With specific models mcp-cli interactive --server sqlite --model gpt-oss # Local reasoning mcp-cli interactive --server sqlite --provider openai --model gpt-5 # Cloud GPT-5 ``` ### 3. Command Mode Unix-friendly interface for automation and scripting: ```bash # Process text with reasoning models mcp-cli cmd --server sqlite --model gpt-oss --prompt "Think through this step by step" --input data.txt # Use GPT-5 for complex reasoning mcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt "Analyze this data" --input data.txt # Execute tools directly mcp-cli cmd --server sqlite --tool list_tables --output tables.json # Pipeline-friendly processing echo "SELECT * FROM users LIMIT 5" | mcp-cli cmd --server sqlite --tool read_query --input - ``` ### 4. Direct Commands Execute individual commands without entering interactive mode: ```bash # List available tools mcp-cli tools --server sqlite # Show provider configuration mcp-cli provider list # Show available models for current provider mcp-cli models # Show models for specific provider mcp-cli models openai # Shows GPT-5, GPT-4, O3 models mcp-cli models anthropic # Shows Claude 4, Claude 3.5 models mcp-cli models ollama # Shows gpt-oss, llama3.3, etc. # Ping servers mcp-cli ping --server sqlite # List resources mcp-cli resources --server sqlite # UI Theme Management mcp-cli theme # Show current theme and list available mcp-cli theme dark # Switch to dark theme mcp-cli theme --select # Interactive theme selector mcp-cli theme --list # List all available themes ``` ## 🤖 Using Chat Mode Chat mode provides the most advanced interface with streaming responses and intelligent tool usage. ### Starting Chat Mode ```bash # Simple startup with default reasoning model (gpt-oss) mcp-cli --server sqlite # Multiple servers mcp-cli --server sqlite,filesystem # With advanced reasoning models mcp-cli --server sqlite --provider openai --model gpt-5 mcp-cli --server sqlite --provider anthropic --model claude-4-1-opus ``` ### Chat Commands (Slash Commands) #### Provider & Model Management ```bash /provider # Show current configuration (default: ollama) /provider list # List all providers /provider config # Show detailed configuration /provider diagnostic # Test provider connectivity /provider set ollama api_base http://localhost:11434 # Configure Ollama endpoint /provider openai # Switch to OpenAI (requires API key) /provider anthropic # Switch to Anthropic (requires API key) /provider openai gpt-5 # Switch to OpenAI GPT-5 /model # Show current model (default: gpt-oss) /model llama3.3 # Switch to different Ollama model /model gpt-5 # Switch to GPT-5 (if using OpenAI) /model claude-4-1-opus # Switch to Claude 4 (if using Anthropic) /models # List available models for current provider ``` #### Tool Management ```bash /tools # List available tools /tools --all # Show detailed tool information /tools --raw # Show raw JSON definitions /tools call # Interactive tool execution /toolhistory # Show tool execution history /th -n 5 # Last 5 tool calls /th 3 # Details for call #3 /th --json # Full history as JSON ``` #### Conversation Management ```bash /conversation # Show conversation history /ch -n 10 # Last 10 messages /ch 5 # Details for message #5 /ch --json # Full history as JSON /save conversation.json # Save conversation to file /compact # Summarize conversation /clear # Clear conversation history /cls # Clear screen only ``` #### UI Customization ```bash /theme # Interactive theme selector with preview /theme dark # Switch to dark theme /theme monokai # Switch to monokai theme # Available themes: default, dark, light, minimal, terminal, monokai, dracula, solarized # Themes are persisted across sessions ``` #### Session Control ```bash /verbose # Toggle verbose/compact display (Default: Enabled) /confirm # Toggle tool call confirmation (Default: Enabled) /interrupt # Stop running operations /servers # List connected servers /help # Show all commands /help tools # Help for specific command /exit # Exit chat mode ``` ### Chat Features #### Streaming Responses with Reasoning Visibility - **🧠 Reasoning Models**: See the AI's thinking process with gpt-oss, GPT-5, Claude 4 - **Real-time Generation**: Watch text appear token by token - **Performance Metrics**: Words/second, response time - **Graceful Interruption**: Ctrl+C to stop streaming - **Progressive Rendering**: Markdown formatted as it streams #### Tool Execution - Automatic tool discovery and usage - Concurrent execution with progress indicators - Verbose and compact display modes - Complete execution history and timing #### Provider Integration - Seamless switching between providers - Model-specific optimizations - API key and endpoint management - Health monitoring and diagnostics ## 🖥️ Using Interactive Mode Interactive mode provides a command shell for direct server interaction. ### Starting Interactive Mode ```bash mcp-cli interactive --server sqlite ``` ### Interactive Commands ```bash help # Show available commands exit # Exit interactive mode clear # Clear terminal # Provider management provider # Show current provider provider list # List providers provider anthropic # Switch provider provider openai gpt-5 # Switch to GPT-5 # Model management model # Show current model model gpt-oss # Switch to reasoning model model claude-4-1-opus # Switch to Claude 4 models # List available models # Tool operations tools # List tools tools --all # Detailed tool info tools call # Interactive tool execution # Server operations servers # List servers ping # Ping all servers resources # List resources prompts # List prompts ``` ## 📄 Using Command Mode Command mode provides Unix-friendly automation capabilities. ### Command Mode Options ```bash --input FILE # Input file (- for stdin) --output FILE # Output file (- for stdout) --prompt TEXT # Prompt template --tool TOOL # Execute specific tool --tool-args JSON # Tool arguments as JSON --system-prompt TEXT # Custom system prompt --raw # Raw output without formatting --single-turn # Disable multi-turn conversation --max-turns N # Maximum conversation turns ``` ### Examples ```bash # Text processing with reasoning models echo "Analyze this data" | mcp-cli cmd --server sqlite --model gpt-oss --input - --output analysis.txt # Use GPT-5 for complex analysis mcp-cli cmd --server sqlite --provider openai --model gpt-5 --prompt "Provide strategic analysis" --input report.txt # Tool execution mcp-cli cmd --server sqlite --tool list_tables --raw # Complex queries mcp-cli cmd --server sqlite --tool read_query --tool-args '{"query": "SELECT COUNT(*) FROM users"}' # Batch processing with GNU Parallel ls *.txt | parallel mcp-cli cmd --server sqlite --input {} --output {}.summary --prompt "Summarize: {{input}}" ``` ## 🔧 Provider Configuration ### Ollama Configuration (Default) Ollama runs locally by default on `http://localhost:11434`. To use reasoning and other models: ```bash # Pull reasoning and other models for Ollama ollama pull gpt-oss # Default reasoning model ollama pull llama3.3 # Latest Llama ollama pull llama3.2 # Llama 3.2 ollama pull qwen3 # Qwen 3 ollama pull qwen2.5-coder # Coding-focused ollama pull deepseek-coder # DeepSeek coder ollama pull granite3.3 # IBM Granite ollama pull mistral # Mistral ollama pull gemma3 # Google Gemma ollama pull phi3 # Microsoft Phi ollama pull codellama # Code Llama # List available Ollama models ollama list # Configure remote Ollama server mcp-cli provider set ollama api_base http://remote-server:11434 ``` ### Cloud Provider Configuration To use cloud providers with advanced models, configure API keys: ```bash # Configure OpenAI (for GPT-5, GPT-4, O3 models) mcp-cli provider set openai api_key sk-your-key-here # Configure Anthropic (for Claude 4, Claude 3.5) mcp-cli provider set anthropic api_key sk-ant-your-key-here # Configure Azure OpenAI (for enterprise GPT-5) mcp-cli provider set azure_openai api_key sk-your-key-here mcp-cli provider set azure_openai api_base https://your-resource.openai.azure.com # Configure other providers mcp-cli provider set gemini api_key your-gemini-key mcp-cli provider set groq api_key your-groq-key # Test configuration mcp-cli provider diagnostic openai mcp-cli provider diagnostic anthropic ``` ### Manual Configuration The `chuk_llm` library configuration in `~/.chuk_llm/config.yaml`: ```yaml ollama: api_base: http://localhost:11434 default_model: gpt-oss openai: api_base: https://api.openai.com/v1 default_model: gpt-5 anthropic: api_base: https://api.anthropic.com default_model: claude-4-1-opus azure_openai: api_base: https://your-resource.openai.azure.com default_model: gpt-5 gemini: api_base: https://generativelanguage.googleapis.com default_model: gemini-2.0-flash groq: api_base: https://api.groq.com default_model: llama-3.1-70b ``` API keys (if using cloud providers) in `~/.chuk_llm/.env`: ```bash OPENAI_API_KEY=sk-your-key-here ANTHROPIC_API_KEY=sk-ant-your-key-here AZURE_OPENAI_API_KEY=sk-your-azure-key-here GEMINI_API_KEY=your-gemini-key GROQ_API_KEY=your-groq-key ``` ## 📂 Server Configuration Create a `server_config.json` file with your MCP server configurations: ```json { "mcpServers": { "sqlite": { "command": "python", "args": ["-m", "mcp_server.sqlite_server"], "env": { "DATABASE_PATH": "database.db" } }, "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/files"], "env": {} }, "brave-search": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-brave-search"], "env": { "BRAVE_API_KEY": "your-brave-api-key" } } } } ``` ## 📈 Advanced Usage Examples ### Reasoning Model Comparison ```bash # Compare reasoning across different models > /provider ollama > /model gpt-oss > Think through this problem step by step: If a train leaves New York at 3 PM... [See the complete thinking process with gpt-oss] > /provider openai > /model gpt-5 > Think through this problem step by step: If a train leaves New York at 3 PM... [See GPT-5's reasoning approach] > /provider anthropic > /model claude-4-1-opus > Think through this problem step by step: If a train leaves New York at 3 PM... [See Claude 4's analytical process] ``` ### Local-First Workflow with Reasoning ```bash # Start with default Ollama/gpt-oss (no API key needed) mcp-cli chat --server sqlite # Use reasoning model for complex problems > Think through this database optimization problem step by step [gpt-oss shows its complete thinking process before answering] # Try different local models for different tasks > /model llama3.3 # General purpose > /model qwen2.5-coder # For coding tasks > /model deepseek-coder # Alternative coding model > /model granite3.3 # IBM's model > /model gpt-oss # Back to reasoning model # Switch to cloud when needed (requires API keys) > /provider openai > /model gpt-5 > Complex enterprise architecture design... > /provider anthropic > /model claude-4-1-opus > Detailed strategic analysis... > /provider ollama > /model gpt-oss > Continue with local processing... ``` ### Multi-Provider Workflow ```bash # Start with local reasoning (default, no API key) mcp-cli chat --server sqlite # Compare responses across providers > /provider ollama > What's the best way to optimize this SQL query? > /provider openai gpt-5 # Requires API key > What's the best way to optimize this SQL query? > /provider anthropic claude-4-sonnet # Requires API key > What's the best way to optimize this SQL query? # Use each provider's strengths > /provider ollama gpt-oss # Local reasoning, privacy > /provider openai gpt-5 # Advanced reasoning > /provider anthropic claude-4-1-opus # Deep analysis > /provider groq llama-3.1-70b # Ultra-fast responses ``` ### Complex Tool Workflows with Reasoning ```bash # Use reasoning model for complex database tasks > /model gpt-oss > I need to analyze our database performance. Think through what we should check first. [gpt-oss shows thinking: "First, I should check the table structure, then indexes, then query patterns..."] [Tool: list_tables] → products, customers, orders > Now analyze the indexes and suggest optimizations [gpt-oss thinks through index analysis] [Tool: describe_table] → Shows current indexes [Tool: read_query] → Analyzes query patterns > Create an optimization plan based on your analysis [Complete reasoning process followed by specific recommendations] ``` ### Automation and Scripting ```bash # Batch processing with different models for file in data/*.csv; do # Use reasoning model for analysis mcp-cli cmd --server sqlite \ --model gpt-oss \ --prompt "Analyze this data and think through patterns" \ --input "$file" \ --output "analysis/$(basename "$file" .csv)_reasoning.txt" # Use coding model for generating scripts mcp-cli cmd --server sqlite \ --model qwen2.5-coder \ --prompt "Generate Python code to process this data" \ --input "$file" \ --output "scripts/$(basename "$file" .csv)_script.py" done # Pipeline with reasoning cat complex_problem.txt | \ mcp-cli cmd --model gpt-oss --prompt "Think through this step by step" --input - | \ mcp-cli cmd --model llama3.3 --prompt "Summarize the key points" --input - > solution.txt ``` ### Performance Monitoring ```bash # Check provider and model performance > /provider diagnostic Provider Diagnostics Provider | Status | Response Time | Features | Models ollama | ✅ Ready | 56ms | 📡🔧 | gpt-oss, llama3.3, qwen3, ... openai | ✅ Ready | 234ms | 📡🔧👁️ | gpt-5, gpt-4o, o3, ... anthropic | ✅ Ready | 187ms | 📡🔧 | claude-4-1-opus, claude-4-sonnet, ... azure_openai | ✅ Ready | 198ms | 📡🔧👁️ | gpt-5, gpt-4o, ... gemini | ✅ Ready | 156ms | 📡🔧👁️ | gemini-2.0-flash, ... groq | ✅ Ready | 45ms | 📡🔧 | llama-3.1-70b, ... # Check available models > /models Models for ollama (Current Provider) Model | Status gpt-oss | Current & Default (Reasoning) llama3.3 | Available llama3.2 | Available qwen2.5-coder | Available deepseek-coder | Available granite3.3 | Available ... and 6 more # Monitor tool execution with reasoning > /verbose > /model gpt-oss > Analyze the database and optimize the slowest queries [Shows complete thinking process] [Tool execution with timing] ``` ## 🔍 Troubleshooting ### Common Issues 1. **Ollama not running** (default provider): ```bash # Start Ollama service ollama serve # Or check if it's running curl http://localhost:11434/api/tags ``` 2. **Model not found**: ```bash # For Ollama (default), pull the model first ollama pull gpt-oss # Reasoning model ollama pull llama3.3 # Latest Llama ollama pull qwen2.5-coder # Coding model # List available models ollama list # For cloud providers, check supported models mcp-cli models openai # Shows GPT-5, GPT-4, O3 models mcp-cli models anthropic # Shows Claude 4, Claude 3.5 models ``` 3. **Provider not found or API key missing**: ```bash # Check available providers mcp-cli provider list # For cloud providers, set API keys mcp-cli provider set openai api_key sk-your-key mcp-cli provider set anthropic api_key sk-ant-your-key # Test connection mcp-cli provider diagnostic openai ``` 4. **Connection issues with Ollama**: ```bash # Check Ollama is running ollama list # Test connection mcp-cli provider diagnostic ollama # Configure custom endpoint if needed mcp-cli provider set ollama api_base http://localhost:11434 ``` ### Debug Mode Enable verbose logging for troubleshooting: ```bash mcp-cli --verbose chat --server sqlite mcp-cli --log-level DEBUG interactive --server sqlite ``` ## 🔒 Security Considerations - **Local by Default**: Ollama with gpt-oss runs locally, keeping your data private - **API Keys**: Only needed for cloud providers (OpenAI, Anthropic, etc.), stored securely - **File Access**: Filesystem access can be disabled with `--disable-filesystem` - **Tool Validation**: All tool calls are validated before execution - **Timeout Protection**: Configurable timeouts prevent hanging operations - **Server Isolation**: Each server runs in its own process ## 🚀 Performance Features - **Local Processing**: Default Ollama provider minimizes latency - **Reasoning Visibility**: See AI thinking process with gpt-oss, GPT-5, Claude 4 - **Concurrent Tool Execution**: Multiple tools can run simultaneously - **Streaming Responses**: Real-time response generation - **Connection Pooling**: Efficient reuse of client connections - **Caching**: Tool metadata and provider configurations are cached - **Async Architecture**: Non-blocking operations throughout ## 📦 Dependencies Core dependencies are organized into feature groups: - **cli**: Terminal UI and command framework (Rich, Typer, chuk-term) - **dev**: Development tools, testing utilities, linting - **chuk-tool-processor**: Core tool execution and MCP communication - **chuk-llm**: Unified LLM provider management with 200+ auto-generated functions - **chuk-term**: Enhanced terminal UI with themes, prompts, and cross-platform support Install with specific features: ```bash pip install "mcp-cli[cli]" # Basic CLI features pip install "mcp-cli[cli,dev]" # CLI with development tools ``` ## 🤝 Contributing We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details. ### Development Setup ```bash git clone https://github.com/chrishayuk/mcp-cli cd mcp-cli pip install -e ".[cli,dev]" pre-commit install ``` ### UI Demo Scripts Explore the UI capabilities powered by chuk-term: ```bash # Terminal management features uv run examples/ui_terminal_demo.py # Output system with themes uv run examples/ui_output_demo.py # Streaming UI capabilities uv run examples/ui_streaming_demo.py ``` ### Running Tests ```bash pytest pytest --cov=mcp_cli --cov-report=html ``` ## 📜 License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## 🙏 Acknowledgments - **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Async-native tool execution - **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - Unified LLM provider management with GPT-5, Claude 4, and reasoning model support - **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Enhanced terminal UI with themes and cross-platform support - **[Rich](https://github.com/Textualize/rich)** - Beautiful terminal formatting - **[Typer](https://typer.tiangolo.com/)** - CLI framework - **[Prompt Toolkit](https://github.com/prompt-toolkit/python-prompt-toolkit)** - Interactive input ## 🔗 Related Projects - **[Model Context Protocol](https://modelcontextprotocol.io/)** - Core protocol specification - **[MCP Servers](https://github.com/modelcontextprotocol/servers)** - Official MCP server implementations - **[CHUK Tool Processor](https://github.com/chrishayuk/chuk-tool-processor)** - Tool execution engine - **[CHUK-LLM](https://github.com/chrishayuk/chuk-llm)** - LLM provider abstraction with GPT-5, Claude 4, O3 series support - **[CHUK-Term](https://github.com/chrishayuk/chuk-term)** - Terminal UI library with themes and cross-platform support", Assign "at most 3 tags" to the expected json: {"id":"14184","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"