LM Studio In Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP
LM Studio In Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP
About the Book
About the Author
Table of Contents
- Preface
- Setup LM Studio
- About the Author
- Requests from the Author
- Why Should We Care About Privacy?
- Introduction to Using the LM Studio Application
- Using the LM Studio Command Line Interface (CLI)
- lms ls
- lms load
- lms unload
- lms get
- Server Status and Control
- Introduction to LM Studio’s Local Inference API
- Using the Python OpenAI Compatibility APIs
- Using the Python lmstudio Package
- Tool Use / Function Calling
- An Initial Example: a Tool That is a Simple Python Function
- Creating a General Purpose Tools/Function Calling Library
- A Technical Introduction to Model Context Protocol and Experiments with LM Studio Using FastMCP
- An Introduction to the Model Context Protocol
- The Local AI Ecosystem: LM Studio as an MCP Host
- Strategic Integration: A Blueprint for Local MCP-Powered Agents
- Architectural Design for a Python-Based MCP Server
- LM Studio Integration and Execution Guide
- Advanced Considerations and Future Trajectories
- MCP Wrap Up
- Preface
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