445 MCP CLI Dbea27bc

🖥️ MCP CLI + ContextForge Gateway

A powerful, feature-rich command-line interface for interacting with Model Context Protocol servers through IBM's ContextForge Gateway. The mcp-cli provides multiple operational modes including chat, interactive shell, and scriptable automation, with support for multiple LLM providers.

With mcp-cli → ContextForge Gateway you can:

• 🔧 Auto-discover tools from your ContextForge Gateway and use them seamlessly
• 🔄 Switch between providers (OpenAI, Anthropic, Ollama) during sessions
• 📊 Export conversation history to JSON for analysis and debugging
• 🤖 Chat with LLMs that automatically invoke Gateway tools and resources
• 📜 Automate workflows with scriptable command-line operations
• 🛠️ Compare modes - chat vs. interactive vs. command-line automation

The mcp-cli supports stdio connections out-of-the-box through the bundled mcpgateway.wrapper bridge, with optional direct SSE access for production environments.

!!! tip "Gateway URL" - Direct installs (uvx, pip, or docker run): http://localhost:4444 - Docker Compose (nginx proxy): http://localhost:8080

🛠 Prerequisites

• Python ≥ 3.11

• uv (recommended) or pip for dependency management

• ContextForge Gateway running locally or remotely (default: http://localhost:4444)

• JWT or Basic Auth credentials for Gateway access

• LLM Provider API keys (optional, for chat mode):

  • OpenAI: OPENAI_API_KEY environment variable
  • Anthropic: ANTHROPIC_API_KEY environment variable
  • Ollama: Local Ollama installation with function-calling capable models

🚀 Installation

Install MCP CLI

git clone https://github.com/chrishayuk/mcp-cli
cd mcp-cli
pip install -e ".[cli,dev]"

Using UV (Recommended)

# Install UV if not already installed
pip install uv

# Clone and install
git clone https://github.com/chrishayuk/mcp-cli
cd mcp-cli
uv sync --reinstall

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

Install ContextForge Gateway

# Clone ContextForge repository
git clone https://github.com/IBM/mcp-context-forge
cd mcp-context-forge

# Install and start the gateway
make venv install serve
# Gateway will be available at http://localhost:4444

⚙️ Configuring Your Server

Create a server_config.json file to define your ContextForge Gateway connection:

Basic Configuration (Local Development)

{
  "mcpServers": {
    "mcpgateway-wrapper": {
      "command": "/path/to/mcp-context-forge/.venv/bin/python",
      "args": ["-m", "mcpgateway.wrapper"],
      "env": {
        "MCP_AUTH": "Bearer <YOUR_AUTH_TOKEN_HERE>",
        "MCP_SERVER_URL": "http://localhost:4444",
        "MCP_TOOL_CALL_TIMEOUT": "120"
      }
    }
  }
}

Docker-based Configuration (Production)

{
  "mcpServers": {
    "mcpgateway-wrapper": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "-e",
        "MCP_SERVER_URL=http://host.docker.internal:4444",
        "-e",
        "MCP_AUTH=${MCPGATEWAY_BEARER_TOKEN}",
        "--entrypoint",
        "uv",
        "ghcr.io/ibm/mcp-context-forge:1.0.0-RC-1",
        "run",
        "--directory",
        "mcpgateway-wrapper",
        "mcpgateway-wrapper"
      ],
      "env": {
        "MCPGATEWAY_BEARER_TOKEN": "your-jwt-token-here"
      }
    }
  }
}

💡 Generate a JWT token for your Gateway

# From your mcp-context-forge directory
python3 -m mcpgateway.utils.create_jwt_token -u admin@example.com --exp 10080 --secret my-test-key

⚠️ Important Notes

• Use the full path to your virtual environment's Python to avoid import errors
• Make sure your ContextForge Gateway is running on the correct port (default: 4444)
• The wrapper requires MCP_SERVER_URL environment variable

🌐 Available Modes

1. Chat Mode (Default)

Natural language interface where LLMs automatically use available tools:

# Default chat mode with OpenAI
export OPENAI_API_KEY="your-api-key"
mcp-cli chat --server mcpgateway-wrapper

# Using Ollama (recommended to avoid OpenAI tool name length limits)
mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest

# Using Anthropic
export ANTHROPIC_API_KEY="your-api-key"
mcp-cli chat --server mcpgateway-wrapper --provider anthropic --model claude-sonnet-4-20250514

2. Interactive Mode

Command-driven shell interface for direct server operations:

mcp-cli interactive --server mcpgateway-wrapper

3. Command Mode

Unix-friendly interface for automation and pipeline integration:

# Process content with LLM
mcp-cli cmd --server mcpgateway-wrapper --input document.md --prompt "Summarize: {{input}}"

# Direct tool invocation
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-list-notifications --raw

# Search for GitHub issues
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-search-issues --tool-args '{"q":"assignee:@me"}' --raw

4. Direct Commands

Run individual commands without entering interactive mode:

# List available tools
mcp-cli tools list --server mcpgateway-wrapper

# Ping the gateway
mcp-cli ping --server mcpgateway-wrapper

# List available prompts
mcp-cli prompts list --server mcpgateway-wrapper

# List available resources
mcp-cli resources list --server mcpgateway-wrapper

🧪 Verify Tool Discovery

Once connected to your ContextForge Gateway, mcp-cli automatically discovers all available tools:

1. Test connection: mcp-cli ping --server mcpgateway-wrapper
2. List tools: mcp-cli tools list --server mcpgateway-wrapper
3. Start Chat Mode: mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest
4. Type /tools - your Gateway tools should list automatically
5. Try asking: "What tools are available?" and the LLM will show discovered tools
6. Test GitHub integration: "What issues have been assigned to me?"

The CLI auto-discovers tools from your Gateway and makes them available across all modes.

🔧 LLM Provider Setup

OpenAI (Has 64-character tool name limitation)

export OPENAI_API_KEY="sk-your-api-key-here"
mcp-cli chat --server mcpgateway-wrapper --provider openai --model gpt-4o-mini

⚠️ Known Issue: OpenAI has a 64-character limit for tool names, but some ContextForge tools exceed this limit (e.g., github-server-add-pull-request-review-comment-to-pending-review is 69 characters).

Ollama (Recommended - No tool name limitations)

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull a function-calling capable model
ollama pull mistral-nemo:latest
# or
ollama pull llama3.2:latest

# Use with mcp-cli
mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest

Anthropic Claude

export ANTHROPIC_API_KEY="sk-ant-your-api-key-here"
mcp-cli chat --server mcpgateway-wrapper --provider anthropic --model claude-3-sonnet

🧪 Basic Usage

Chat Mode Commands

In chat mode, use these slash commands for enhanced functionality:

General Commands

• /help - Show available commands
• /quickhelp or /qh - Quick reference guide
• exit or quit - Exit chat mode

Provider & Model Management

• /provider - Show current provider and model
• /provider list - List all configured providers
• /provider <name> - Switch to different provider
• /model <name> - Switch to different model

Tool Management

• /tools - Display all available tools from your Gateway
• /tools --all - Show detailed tool information
• /toolhistory or /th - Show tool call history

Conversation Management

• /conversation or /ch - Show conversation history
• /save <filename> - Save conversation to JSON file
• /compact - Condense conversation history

Example Chat Interactions

> what issues have been assigned to me?
[Tool Call: github-server-get-me]
[Tool Call: github-server-search-issues with q="assignee:username"]

> what files are in my Downloads folder?
[Tool Call: filesystem-downloads-list-directory]

> create a memory about this conversation
[Tool Call: memory-server-store-memory]

> what time is it in London?
[Tool Call: time-server-get-system-time with timezone="Europe/London"]

Interactive Mode Commands

In interactive mode, use these commands:

• /help - Show available commands
• /tools or /t - List/call tools interactively
• /resources or /res - List available resources
• /prompts or /p - List available prompts
• /servers or /srv - List connected servers
• /ping - Ping connected servers

Command Mode Options

• --input - Input file path (use - for stdin)
• --output - Output file path (use - for stdout)
• --prompt - Prompt template with {{input}} placeholder
• --tool - Directly call a specific tool
• --tool-args - JSON arguments for tool call
• --provider - Specify LLM provider
• --model - Specify model to use
• --raw - Output raw response without formatting

🔧 Advanced Configuration

Environment Variables

# ContextForge Gateway connection
export MCP_AUTH="Bearer your-jwt-token"
export MCP_SERVER_URL="http://localhost:4444"

# LLM Provider API keys
export OPENAI_API_KEY="sk-your-openai-key"
export ANTHROPIC_API_KEY="sk-ant-your-anthropic-key"

# Default provider settings
export LLM_PROVIDER="ollama"
export LLM_MODEL="mistral-nemo:latest"

Troubleshooting Common Issues

"ModuleNotFoundError: No module named 'mcpgateway'"

Solution: Use the full path to your virtual environment's Python:

{
  "mcpServers": {
    "mcpgateway-wrapper": {
      "command": "/Users/username/path/to/mcp-context-forge/.venv/bin/python",
      "args": ["-m", "mcpgateway.wrapper"],
      "env": { ... }
    }
  }
}

"MCP_SERVER_URL environment variable is required"

Solution: Ensure your server_config.json includes the required environment variables in the env section.

OpenAI Tool Name Length Error

Error: string too long. Expected a string with maximum length 64

Solution: Use Ollama or Anthropic instead:

mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest

Model doesn't support tools

Error: does not support tools (status code: 400)

Solution: Use a function-calling capable model:

# Pull compatible models
ollama pull mistral-nemo:latest
ollama pull llama3.2:latest

# Use in mcp-cli
mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest

📈 Advanced Usage Examples

GitHub Integration

# Get your GitHub profile
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-get-me --raw

# List notifications
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-list-notifications --raw

# Search for issues assigned to you
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-search-issues \
  --tool-args '{"q":"assignee:@me is:open"}' --raw

# Create a new issue
mcp-cli cmd --server mcpgateway-wrapper --tool github-server-create-issue \
  --tool-args '{"owner":"username","repo":"repository","title":"New Issue","body":"Issue description"}' --raw

File System Operations

# List allowed directories
mcp-cli cmd --server mcpgateway-wrapper --tool filesystem-downloads-list-allowed-directories --raw

# Read a file
mcp-cli cmd --server mcpgateway-wrapper --tool filesystem-downloads-read-file \
  --tool-args '{"path":"/path/to/file.txt"}' --raw

# Search for files
mcp-cli cmd --server mcpgateway-wrapper --tool filesystem-downloads-search-files \
  --tool-args '{"path":"/Users/username/Downloads","pattern":"*.pdf"}' --raw

Memory Management

# Store a memory
mcp-cli cmd --server mcpgateway-wrapper --tool memory-server-store-memory \
  --tool-args '{"content":"Important project note","bucket":"work"}' --raw

# Get memories
mcp-cli cmd --server mcpgateway-wrapper --tool memory-server-get-memories \
  --tool-args '{"bucket":"work"}' --raw

Time Operations

# Get current time
mcp-cli cmd --server mcpgateway-wrapper --tool time-server-get-system-time --raw

# Convert time zones
mcp-cli cmd --server mcpgateway-wrapper --tool time-server-convert-time \
  --tool-args '{"from_timezone":"UTC","to_timezone":"America/New_York","time":"2025-01-01T12:00:00Z"}' --raw

🔗 Integration with ContextForge Gateway

The mcp-cli integrates with ContextForge Gateway through multiple connection methods:

Local Development Setup

1. Start the Gateway:

   cd mcp-context-forge
   make serve  # Starts on http://localhost:4444
   

2. Configure mcp-cli:

   {
     "mcpServers": {
       "mcpgateway-wrapper": {
         "command": "/path/to/mcp-context-forge/.venv/bin/python",
         "args": ["-m", "mcpgateway.wrapper"],
         "env": {
           "MCP_AUTH": "Bearer <your-jwt-token>",
           "MCP_SERVER_URL": "http://localhost:4444"
         }
       }
     }
   }
   

3. Test the connection:

   mcp-cli ping --server mcpgateway-wrapper
   

Production Docker Setup

Use the official Docker image for production environments:

# Start the gateway
docker run -d --name mcpgateway \
  -p 4444:4444 \
  -e HOST=0.0.0.0 \
  -e JWT_SECRET_KEY=my-secret-key \
  -e BASIC_AUTH_USER=admin \
  -e BASIC_AUTH_PASSWORD=changeme \
  -e PLATFORM_ADMIN_EMAIL=admin@example.com \
  -e PLATFORM_ADMIN_PASSWORD=changeme \
  -e PLATFORM_ADMIN_FULL_NAME="Platform Administrator" \
  ghcr.io/ibm/mcp-context-forge:1.0.0-RC-1

# Generate token
export MCPGATEWAY_BEARER_TOKEN=$(docker exec mcpgateway python3 -m mcpgateway.utils.create_jwt_token --username admin@example.com --exp 10080 --secret my-secret-key)

# Test connection
curl -H "Authorization: Bearer $MCPGATEWAY_BEARER_TOKEN" http://localhost:4444/tools

📝 Available Tool Categories

Your ContextForge Gateway provides these tool categories:

🗂️ Filesystem Tools

• Downloads & Documents: Read, write, edit, search files
• Directory Operations: List, create, move files and directories
• File Management: Get file info, create directory trees

🐙 GitHub Integration

• Issue Management: Create, update, list, search issues
• Pull Requests: Create, review, merge, comment on PRs
• Repository Operations: Fork, create, manage repositories
• Notifications: List, manage, dismiss notifications
• Code Analysis: Search code, get commits, manage branches

🧠 Memory Server

• Memory Storage: Store and retrieve contextual memories
• Bucket Management: Organize memories in buckets
• Memory Querying: Search and filter stored memories

⏰ Time Operations

• System Time: Get current time in any timezone
• Time Conversion: Convert between different timezones

📊 Features Comparison

📚 Further Reading

• mcp-cli GitHub → https://github.com/chrishayuk/mcp-cli
• CHUK-MCP Protocol → https://github.com/chrishayuk/chuk-mcp
• ContextForge Gateway → https://github.com/IBM/mcp-context-forge
• MCP Specification → https://modelcontextprotocol.io/

🎯 Quick Start Checklist

• Install mcp-cli: pip install -e ".[cli,dev]"
• Install ContextForge Gateway
• Start gateway: make serve (runs on localhost:4444)
• Create server_config.json with correct Python path
• Generate JWT token for authentication
• Test connection: mcp-cli ping --server mcpgateway-wrapper
• Install Ollama and pull a compatible model (recommended)
• Start chat: mcp-cli chat --server mcpgateway-wrapper --provider ollama --model mistral-nemo:latest
• Try asking: "What tools are available?" or "What issues have been assigned to me?"