Getting Started with Model Context Protocol (MCP) (eBook)

A Beginner's Guide to Building Structured AI Agent Systems

Eron Valdric (Autor)

eBook Download: EPUB

2025 | 1. Auflage
200 Seiten
Publishdrive (Verlag)
978-0-00-091653-2 (ISBN)

Infos

Getting Started with Model Context Protocol (MCP): A Beginner's Guide to Building Structured AI Agent Systems

Modern software systems are shifting rapidly from prompt-based experiments to structured, protocol-driven agent architectures. If you're looking to build intelligent applications that are clear, maintainable, and scalable, this guide to Model Context Protocol (MCP) is the perfect starting point.

Build Smarter Agent Systems from the Ground Up

This practical guide introduces the essential concepts behind MCP - a framework designed to help developers build role-based, tool-integrated, and context-aware agent workflows. Whether you're designing your first autonomous system or refining an existing setup, you'll find step-by-step explanations, schema templates, and workflow patterns that bring clarity to complex development tasks.

What This Book Covers

Clear explanations of core MCP roles: Planner, Retriever, Executor, Validator, Critic

How to define tool manifests, memory structures, and execution contracts

How to structure agent workflows using YAML/JSON DSL formats

Integration techniques for tools, APIs, and shared memory resources

Common mistakes to avoid when building multi-step agent systems

Real-world implementation patterns in Python and other practical formats

A Resource for Engineers and Builders

This book is written for developers, engineers, and technical teams who want to create reliable and reusable agent systems. It provides foundational knowledge and field-tested methods that can be applied across many modern platforms.

Inside You'll Find

Dozens of example templates and configuration patterns

Full walk-throughs for building complete agent pipelines

A comprehensive glossary and troubleshooting guide

Appendices covering schema design, DSL rules, and evaluation practices

Guidance on future trends like autonomous runtimes and memory federation

If you're ready to move beyond trial-and-error prompt design and start building structured, dependable systems - this book gives you the foundation to do exactly that.

Build with clarity. Deploy with confidence. Start your journey with MCP today.

Textauszug

Chapter 2: The Evolution of Agent Architectures

2.1 From Prompts to Modular Agents

Introduction

The first wave of LLM applications was dominated by single-shot prompting and manually designed prompt chains. While these approaches made it easy to experiment, they lacked structure, testability, and composability. As tasks grew in complexity, developers began modularizing LLM interactions into agent components each with defined behavior, access, and responsibilities.

This shift—from prompt monoliths to modular agents—laid the groundwork for protocol-driven systems like MCP.

Concept Breakdown

Definition:
Modular Agent — A self-contained LLM-based component that operates within a system, responsible for a specific task or function, guided by context and tool access.

Definition:
Prompt Chaining — The ad-hoc practice of feeding LLM outputs into downstream prompts without role definition or context schema.

Table: Prompt Chaining vs. Modular Agent Systems

Feature

Prompt Chaining

Modular Agents (MCP-Aligned)

Execution Model

Sequential, implicit

Role-based, explicit

Reusability

Poor

High

Debuggability

Manual logs, unclear

Role logs, context schema

Testing Scope

Entire chain

Per-agent unit testing

Agent Role Separation

None

Strict

Flowchart: System Evolution

Prompt Chaining →

↓

Hardcoded Roles →

↓

Output Inconsistency →

↓

Modular Agent Systems (Planner → Executor → Critic) →

↓

Protocol Adoption (MCP)

Tip: Modular agents follow the principle of single responsibility — each agent should handle one function well and defer others.

Application Layer

Example: Building a Document Summary System

Version

Implementation

Prompt-Only

A single prompt: “Search the web and summarize the top result.”

Modular Agent

Role 1: Retriever → Role 2: Executor → Role 3: Critic

Retriever Context Example:

context:

role: "retriever"

goal: "Find recent research on AI reliability"

input: "latest AI reliability papers"

tools:

- name: "scholar_search"

Each component is replaceable, debuggable, and testable on its own.

Challenges & Pitfalls

Challenge

Description

Under-Modularization

Agents with overloaded responsibilities fail in multi-task settings

Poor Handoff Design

Improper output/input mapping between agents breaks workflows

Tool Assumptions

Agents may try to call tools not scoped to them

Lack of Memory Awareness

Agents unaware of past steps reduce system consistency

Dependency Entanglement

Without schema, tightly coupled logic causes downstream breakage

Key Takeaways

● Prompt chaining is not scalable for complex or multi-step tasks

● Modular agents improve maintainability, testing, and traceability

● Each agent in MCP has a defined role and access scope

● Modular design aligns with how real-world software systems are built

● This modularity is enforced via schema in MCP

2.2 Function Calling and Tool-Augmented Execution

Introduction

The ability to call tools — such as APIs, scripts, or functions — is central to agent execution. LLMs like GPT-4, Claude, and open-source models now support function calling, which enables agents to act with real capabilities beyond language prediction.

Tool-augmented execution transforms agents from responders to actors — capable of retrieving data, making calculations, posting updates, and more.

Concept Breakdown

Definition:
Function Calling — A mechanism by which an LLM outputs a structured JSON object corresponding to an API schema, rather than plain text.

Definition:
Tool-Augmented Agent — An agent that can invoke predefined external tools, APIs, or utilities during execution, based on structured context.

Table: Function Calling Properties

Property

Description

Schema Validation

Inputs/outputs must match a declared interface

Execution Delegation

Model calls function; system executes it

Result Injection

Tool output is appended to the agent's context

Retry Capability

Tool can be re-invoked or replaced on failure

Tip: Always define tool input/output schemas. This improves interpretability and helps prevent runtime errors.

Application Layer

Let’s say your agent needs to calculate compound interest.

Tool Manifest:

{

"name": "calc_interest",

"description": "Calculate compound interest",

"input_schema": {

"principal": "float",

"rate": "float",

"time": "int"

},

"output_schema": {

"total": "float"

}

}

Context Example (Executor Role):

context:

role: "executor"

goal: "Calculate interest"

input: "principal=1000, rate=5%, time=2 years"

tools:

- name: "calc_interest"

LLM Output (Function Call Intent):

{

"tool_call": {

"name": "calc_interest",

"arguments": {

"principal": 1000,

"rate": 0.05,

"time": 2

}

}

}

The system runs the call, appends the result, and the agent proceeds with full tool feedback.

Challenges & Pitfalls

Challenge

Description

No Schema Enforcement

Functions used without strict definitions lead to unpredictable behavior

Tool Overuse

Relying on too many tools can slow system execution

API Failures

Retry logic must handle rate limits and tool outages

Output Injection Errors

Tool results not parsed or mapped correctly to next step

Permissions Leakage

Agents accessing unauthorized tools create security risk

Key Takeaways

● Function calling gives agents real-world actionability

● Tool manifests must be declared formally and enforced by schema

● MCP supports tool-augmented execution through context and manifest alignment

● With proper definition, tools can be reused, traced, and debugged

● This is the beginning of moving from text generation to structured agent behavior

2.3 The Emergence of Role-Based Design

Introduction

As developers pushed the boundaries of what LLMs could accomplish, they encountered a familiar software engineering truth: clarity and separation of responsibility are essential. The traditional practice of embedding logic into prompts failed to scale or generalize.

This led to the rise of role-based agent design, where each AI agent performs a clearly defined function — a model adopted and formalized in MCP.

Concept Breakdown

Definition:
Role-Based Design — A system architecture that assigns each AI agent a single, bounded responsibility (e.g., planning, retrieval, execution, validation), executed independently through structured context.

Definition:
Role Contract — A specification that outlines what an agent is supposed to do, the input schema it accepts, and the output it must...

Produkt-Details

• Erscheint lt. Verlag: 21.6.2025
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• ISBN-10: 0-00-091653-6 / 0000916536
• ISBN-13: 978-0-00-091653-2 / 9780000916532