IBM i MCP Server

# IBM i MCP Agents: Agno AI agents for IBM i system administration and monitoring built with Agno's AgentOS framework and Model Context Protocol (MCP) tools. This project provides intelligent agents that can analyze IBM i system performance, manage resources, and assist with administrative tasks. ## What is this project? The IBM i MCP Agents project provides Python-based intelligent agents that leverage MCP tools to perform system administration tasks on IBM i systems. ### Key Features - **Multiple Specialized Agents**: Six purpose-built agents for different IBM i tasks - **Multi-Model Support**: Works with OpenAI, Anthropic Claude, IBM WatsonX, and local Ollama models - **MCP Integration**: Connects to the IBM i MCP Server for system operations - **Persistent Memory**: Agents maintain context across sessions using SQLite - **Interactive CLI**: Simple command-line interface for agent interaction ### Available Agents 1. **Performance Agent** - Monitor and analyze system performance metrics (CPU, memory, I/O) 2. **Discovery Agent** - High-level system discovery, inventory, and service summaries 3. **Browse Agent** - Detailed exploration of system services by category or schema 4. **Search Agent** - Find specific services, programs, or system resources 5. **Web Agent** - General web search using DuckDuckGo (no MCP required) 6. **Agno Assist** - Learn about the Agno framework and agent development ## Requirements - **Python 3.13+** - The project requires Python 3.13 or newer - **uv** - Python package manager for installing dependencies and managing virtual environments ([Install uv](https://astral.sh/uv/)) - **IBM i MCP Server** - Must be installed and running on your system - **API Keys** - For your chosen LLM provider (OpenAI, Anthropic, WatsonX, or Ollama) ## Setup Guide Follow these step-by-step instructions to set up and run the IBM i Agno MCP Agents. ### Step 1: Install Prerequisites **1.1 Install Python 3.13+** ```bash # Check your Python version python --version # or python3 --version # If you need to install Python 3.13+, visit: # https://www.python.org/downloads/ ``` **1.2 Install uv (Python package manager)** ```bash # On macOS and Linux: curl -LsSf https://astral.sh/uv/install.sh | sh # On Windows (PowerShell): powershell -c "irm https://astral.sh/uv/install.ps1 | iex" # Alternative: Install via pip pip install uv ``` ### Step 2: Set Up the IBM i MCP Server Ensure you have the IBM i MCP Server installed and running. > [!NOTE] > **Follow the MCP Server installation guide →** [Quickstart Guide](../../../README.md#-quickstart) > > **Configure the server →** [Server Configuration Guide](../../../README.md#-configuration) **2.1 Install dependencies and build the server:** ```bash cd ibmi-mcp-server npm install npm run build ``` **2.2 Start the MCP server:** ```bash npx ibmi-mcp-server --transport http --tools ./tools ``` The server will start on `http://127.0.0.1:3010/mcp` by default. ### Step 3: Configure Environment Variables Create a `.env` file in the `agents/frameworks/agno` directory with your API keys: ```bash cd agents/frameworks/agno touch .env ``` **3.1 Add API keys for your chosen provider(s):** ```bash # OpenAI (for GPT-4, GPT-4o models) OPENAI_API_KEY=sk-your-openai-api-key # Anthropic (for Claude models) ANTHROPIC_API_KEY=sk-ant-your-anthropic-api-key # Ollama (local models - no API key needed) # Ensure Ollama is installed and running: https://ollama.ai # Start with: ollama serve ``` **Note:** You only need API keys for the providers you plan to use. ### Step 4: Run an Agent **4.1 List available agents:** ```bash cd agents/frameworks/agno uv run ibmi_agentos.py --list ``` **4.2 Run an agent with your chosen model:** ```bash # OpenAI GPT-4o uv run ibmi_agentos.py --agent performance --model openai:gpt-4o # Anthropic Claude Sonnet uv run ibmi_agentos.py --agent discovery --model anthropic:claude-sonnet-4-5 # Local Ollama model uv run ibmi_agentos.py --agent search --model ollama:gpt-oss:20b ``` **4.3 Interact with the agent:** - Type your questions or requests at the prompt - The agent will use IBM i MCP tools to fulfill your requests - Type `exit` or `quit` to end the session ## Usage Examples ### Performance Monitoring ```bash uv run ibmi_agentos.py --agent performance --model openai:gpt-4o ``` Example questions: - "What is the current CPU utilization?" - "Show me memory usage trends" - "Are there any performance bottlenecks?" ### System Discovery ```bash uv run ibmi_agentos.py --agent discovery --model openai:gpt-4o ``` Example questions: - "Give me an overview of the system services" - "What databases are available?" - "List all active subsystems" ### Detailed Browsing ```bash uv run ibmi_agentos.py --agent browse --model openai:gpt-4o ``` Example questions: - "Show me details about the QSYS library" - "Explore the database schemas" - "What's in the QTEMP library?" ### System Search ```bash uv run ibmi_agentos.py --agent search --model openai:gpt-4o ``` Example questions: - "Find all programs named CUST*" - "Search for services containing 'SQL'" - "Locate file CUSTOMER in any library" ## Advanced Options ### Debug Mode Enable debug output to troubleshoot issues: ```bash uv run ibmi_agentos.py --agent performance --model openai:gpt-4o --debug ``` ### Custom MCP Server URL If your MCP server runs on a different host or port: ```bash uv run ibmi_agentos.py --agent performance --model openai:gpt-4o --mcp-url http://localhost:8080/mcp ``` ## Architecture Overview ### How It Works 1. **Agent Selection**: You choose an agent specialized for a specific task (performance, discovery, etc.) 2. **MCP Connection**: The agent connects to the IBM i MCP Server via HTTP 3. **Tool Filtering**: Each agent only has access to relevant tools (e.g., performance agent gets performance tools) 4. **Model Execution**: Your chosen LLM model processes requests and generates tool calls 5. **Persistent Memory**: Agent sessions and memory are stored in SQLite (`tmp/ibmi_agents.db`) ### Supported Models | Provider | Model Examples | Usage | |----------|---------------|-------| | **OpenAI** | gpt-4o, gpt-4o-mini, gpt-4-turbo | `openai:gpt-4o` | | **Anthropic** | claude-sonnet-4-5, claude-opus-4 | `anthropic:claude-sonnet-4-5` | | **WatsonX** | llama-3-3-70b, granite-3-3-8b | `watsonx:llama-3-3-70b-instruct` | | **Ollama** | llama3.2, gpt-oss, mistral | `ollama:llama3.2` |