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LM Studio in Action: Building Safe, Private AI with LLMs, Function Calling and Agents with MCP

This book is 100% completeLast updated on 2026-07-31
+6,305 words in the last 30 days
Mark Watson

LM Studio is a great tool for running LLM models locally on your own computer. Tech overviews and Python examples. Deep dive into using Model Context Protocol (MCP)

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Mark Watson

Mark Watson is a consultant specializing in LLMs, deep learning, machine learning, knowledge graphs, and general artificial intelligence software development. He uses Common Lisp, Clojure, Python, Java, Haskell, and Ruby for development.

He is the author of 20+ published books on Artificial Intelligence, Deep Learning, Java, Ruby, Machine Learning, Common LISP, Clojure, JavaScript, Semantic Web, NLP, C++, Linux, and Scheme. He has 55 US Patents.

Mark's consulting customer list includes: Google, Capital One, Olive AI, CompassLabs, Disney, Sitescout.com, Embed.ly, and Webmind Corporation.

Mark wrote ten traditional published books for McGraw Hill, Springer Verlag, J Wiley, and Morgan Kaufman publishers before adopting the LeanPub self-publishing platform.

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Contents

Table of Contents

Preface

  1. Setup LM Studio
  2. What’s New Since the First Edition of This Book
  3. About the Author
  4. Requests from the Author
  5. Why Should We Care About Privacy?

Introduction to Using the LM Studio Application

Using the LM Studio Command Line Interface (CLI)

  1. lms ls
  2. lms load
  3. lms unload
  4. lms get
  5. lms chat
  6. lms import
  7. Server Status and Control
  8. lms runtime
  9. lms log stream
  10. Headless Mode: lms daemon
  11. LM Link: lms link
  12. Developing and Publishing

Introduction to LM Studio’s Local Inference API

  1. Using the Python OpenAI Compatibility APIs
  2. Using the Python lmstudio Package
  3. Three Python SDK API Styles
  4. Streaming Responses
  5. Structured Output
  6. Embeddings
  7. The Anthropic Compatibility API
  8. The Responses API
  9. API Authentication
  10. Idle TTL and Auto-Evict
  11. Optional Practice Problems

Tool Use / Function Calling

  1. Native Tool Use via the OpenAI-Compatible API
  2. The Python SDK .act() API
  3. A Worked Example: DuckDuckGo Web Search
  4. Manual Tool Use: Understanding the Basics
  5. Optional Practice Problems

A Technical Introduction to Model Context Protocol and Experiments with LM Studio Using FastMCP

  1. An Introduction to the Model Context Protocol
  2. The Local AI Ecosystem: LM Studio as an MCP Host
  3. Strategic Integration: A Blueprint for Local MCP-Powered Agents
  4. Architectural Design for a Python-Based MCP Server
  5. LM Studio Integration and Execution Guide
  6. Advanced Considerations and Future Trajectories
  7. MCP Wrap Up
  8. Optional Practice Problems

Local Agents With LM Studio Bionic

  1. Two Kinds of Projects: Work and Code
  2. Choosing a Model: Local, Cloud, and Remote
  3. Voice Input
  4. Bionic and Your LM Studio Model Library
  5. When to Use Bionic vs LM Studio Desktop
  6. Wrap Up

Headless Deployment with llmster

  1. Installing llmster on a Linux Server
  2. Running as a systemd Service
  3. JIT Loading for Multi-Model Serving
  4. API Authentication for Network Deployments
  5. Serving Models to Remote Clients with LM Link
  6. Wrap Up

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