Powering Spring AI with Model Context Protocol Integration

Powering Spring AI with Model Context Protocol
Integration
SOUJAVA - Java 30Y #CelebrationWeek
Juarez Barbosa Junior @juarezjunior
Senior Principal Java Developer Evangelist
ORACLE
Copyright © 2025, Oracle and/or its affiliates

Juarez Barbosa Junior
Senior Principal Java Developer Evangelist @ Oracle
• Speaking from Dublin, Ireland
• 30 years of experience in SW Engineering & DevRel
• Oracle, Microsoft, IBM, Nokia, Unisys, Accenture, startups
• Microsoft Azure Developer Relations Lead
• IBM Watson Tech Evangelist & Cloud Rockstar
• IBM Mobile Tech Evangelist & Global Thought Leader
• Nokia Developers Global Champion
• Java, Python, Cloud, DevOps, SRE, Cloud-native, IoT, AI, Blockchain, Rust
• Speaker at conferences
• Oracle CloudWorld, Oracle Code, Microsoft Ignite & TechX, jPrime, JCON,
DevConf.cz, GeeCon, DevOpsDays, DeveloperWeek, DevOps Institute,
CloudLand, DWX, The Developer’s Conference (TDC), Sec4Dev, JSNation,
NodeConf, Conf42, Shift Conf, Global Azure, Open-Source Lisbon,
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OpenSourceNorth, WeAreDevelopers, Global Software Architecture
Summit, Java Day Istanbul, JUGs, OUGs, GDGs, meetups, hackathons, and
customer engagements.
@juarezjunior
@juarezjunior

Confidential - Oracle Restricted
Agenda
• Spring AI - overview
• Spring AI - basics (chat, image, multimodal)
• Oracle AI Vector Search
• Spring AI + Oracle AI Vector Search + RAG
• Spring AI - Tool calling
• Agentic Protocols - MCP, A2A, ACP
• MCP - Model Context Protocol
• Spring AI - MCP
• Spring AI - Agentic Patterns
• Technical References
• Reference Architecture for GenAI
• Oracle Developer Relations

AI and GenAI –
Synthetic Content
Brasilia in Brazil pictured in a futuristic cityscape

Artificial Intelligence / GenAI
• Core concepts in a nutshell
• GenAI - Artificial intelligence algorithms and transformer models with
the capability of generating content.
• Content - Text, images, videos, code, and others.
• Generation – a result of an input prompt (prompt engineering).
• Ethical issues – misuse, deep fakes, bias and discrimination,
automation and job replacements, cybersecurity and cybercriminals.

Artificial Intelligence / AGI / ASI
• Core concepts in a nutshell
• Apps – Applied in the fields of content development, entertainment,
drug discovery, software development, healthcare, material
generation sciences, and others.
• AI is now a commodity!
• Artificial General Intelligence (AGI) – ultimate goal
• Artificial Super Intelligence (ASI)

Spring AI - Connecting your enterprise
data and APIs with AI models

Spring AI (1.0.0)
• Spring AI (AI Concepts)
• A Java app framework for building AI-related applications. It provides a robust abstraction layer
that encapsulates many of the complexities required to interact with various GenAI providers,
such as OpenAI, Cohere, Google Vertex AI/Gemini, and others.
• API support across AI providers for chat, text-to-image, embedding models, agents, etc.
• Its goal is to apply to the AI domain Spring ecosystem design principles, such as portability and
modular design, and promote using POJOs as the building blocks of an application to the AI
domain.
• https://spring.io/projects/spring-ai

Spring AI
• Spring AI – Blazingly Fast Performance
• GraalVM
• Native Image - compile Java code ahead of
time (AOT) to a binary – a native executable
• Developing an Oracle JDBC app with GraalVM
Native Image
• mvn -Pnative native:compile
• ./target/spring-ai-graalvm-oai.exe

Spring AI
• Spring AI – Blazingly Fast Performance
• Spring with Virtual Threads
• Add the following entry in the application.properties file:
spring.threads.virtual.enabled=true
• Class VirtualThreadTaskExecutor
• Embracing Virtual Threads
• All together now: Spring Boot 3.2, GraalVM native images, Java 21, and virtual
threads with Project Loom

Spring AI
• The Spring AI API covers a wide range of functionalities
• Chat Completion API
• Embeddings API
• Image Generation API
• Speech / Transcription API
• Vector Databases
• Function Calling / Tools API
• MCP – Model Context Protocol

Chat Completion - Spring AI with Open AI
• Demo – Chat - Direct LLM calls

Image Generation - Spring AI with Open AI
• Demo – Image Generation

Multimodality - Spring AI with Open AI
• Demo - Multimodality

Oracle Database 23ai
Next-Generation Converged Database Services
Over 300 major new features plus thousands of enhancements
Available on OCI and Azure
Major focus on
• AI for Data features
• Developer/Analyst features
• Mission-Critical features
Addresses data management pain points that have frustrated customers forever

Oracle Database 23ai - AI for Data
Algorithmic AI AI Vector Search Augmented Generative AI (LLMs)
Distributed AI AI Storage AI Developer Tools

Oracle Database 23ai Vector Search

Converged Database
Add vector search to your business database
NEW Vector data type for storing vector embeddings
NEW SQL syntax and functions express similarity search with
ease
NEW Approximate search indexes packaged and tuned for
high performance and quality
There is no need to move and synchronize data,
manage multiple products, etc.
Better Together: Business Data and Business Vectors
20

21 Copyright © 2025, Oracle and/or its affiliates
How
Oracle AI Vector Search Works

Databases have
traditionally
performed value-
based searches
Orders
Customer
records
Products
Find revenue
by products

Growing volume
of unstructured
business data that
must be searched by
semantics
Find products that
match a photo or
description
Orders
Customer
records
Products
Medical
forms
Invoices
Social data
images
Videos
Phone call
scripts
The “all data” opportunity
JSON

AI Vector Search A new breakthrough
technology to help find
documents, images, and
other unstructured data
Based on their semantic
content, rather than
their words or pixels

Vector
33
42
16
21
50
An AI Vector is a sequence of
numbers, called dimensions, used to
capture the important “features” of
the data
Represent the semantic content of
data, not the actual words in a
document or pixels in an image
Produced by AI Deep Learning
Models known as Embedding
Models

The main operation on vectors is the
Mathematical Distance between them
8
2
1
3
3
2
6
2
Distance (Euclidean Squared)
= ((3-2)2+(1-6)2+(2-2)2+(8-3)2)
There are many mathematical distance formulas

Laptop Gen 32
Support Rep: Jane Doe
Email: jane@doe.com
• Severity 1
• Spontaneous reboot
• Resolved
• Applied OS Update 42
Support Incident
An Example Business Scenario:
The vector for a support incident could be ...
Vector
33
42
16
21
50

Laptop Gen 32
Support Rep: Jane Doe
Email: jane@doe.com
• Severity 1
• Spontaneous reboot
• Resolved
• Applied OS Update 42
Support Incident
An Example Business Scenario:
The vector for a support incident could be ...
Each dimension
(number), represents a
different feature of the
support incident
Vector Features
Severity
Current OS version
Symptoms
Product
Status
33
42
16
21
50
Note: Features are often chosen by ML algorithms and are not as simple as shown here

29
Collapsing vectors into 2 dimensions
instead of hundreds could look like this
d2
d1
Support Incidents
for Laptops running
slowly
Support Incident
for Desktop crashing

30
d2
d1
Not as Similar
Similar
Similarity Property: Images that are more similar
produce vectors that are closer together

31
d2
d1
Similarity Property: Images that are more similar
produce vectors that are closer together
Documents and
images also work the
same way
Document vectors that
represent similar
content are closer
than those
representing dissimilar
content
blackberry
strawberry
apple
raspberry
pear
plum
cat
kitten
dog
puppy
wolf
lion
Texas
California
New York
kiwi
elephant
Animals
Fruit
States
tiger

Now that we know what vectors are, let’s talk
about how they are used
Vector Search works best when combined
with relational search to solve
business problems
33
42
16
21
50

Use cases
What do embeddings and vector databases enable?
Similarity Search
Match candidates with jobs
Image and video retrieval
Find patients with similar issues
Legal e-discovery
Content-Based
Filtering
Personalized recommendation
Find retail items from image
Natural Language
Processing
Text Classification and Clustering
SQL generation
Data Analytics
Anomaly detection
Pattern recognition
Computer Vision
Face recognition
Biometric identification
Object detection
Biomedical Research
Gene/DNA similarity analysis
Molecular structure search
Geographic
Information Systems
Spatial analysis
Map rendering
Industrial Applications
Quality control
Predictive maintenance
Machinery malfunction

What is Vector Generation or Embedding?
• The process of converting unstructured data (text, images, etc.) into vectors
• Vectors are generated using deep-learning embedding models
• Vector embedding models can be obtained commercially, from Open Source, or can be created
• There may be no BEST embedding model, and different data may need different models
• No one size fits all
• Evaluate a model based on your data
Embedding
Model
Unstructured
Data
Deep-learning
ML models
Vectors

Perform AI Vector Search on VECTOR columns using SQL
AI Vector Search Highlights
GENERATE
STORE
SEARCH
INDEX
INTEGRATE
35
Generate vector embeddings from unstructured data
Store vectors in table columns using new VECTOR type
Build approximate vector indexes on VECTOR columns
Integrate with Mission-Critical Enterprise Capabilities

AI Vector Search powers Complete Gen AI pipeline
Data Sources Document
Loaders
Document Transformation
(e.g., Text Splitting,
Summarization)
Embedding
Models
Vector
Database
Similarity
Search
LLMs User
AI Vector Search in Oracle 23ai Database
RAG
1. Native Database APIs for Gen-AI, from Document Processing to
REST call-outs to LLM Providers
2. Tight integration with 3rd party frameworks such as Langchain

Spring AI - Vector Search + RAG

Retrieval-Augment
Generation (RAG)
• RAG (Retrieval-Augment Generation) with Oracle Cloud / Oracle Database
23ai
• LLMs' baseline knowledge might have gaps based on their training data. If you ask
an LLM to write about a recent trend or event, the LLM won't know what you're
talking about, so you may get meaningless responses.
• There's a way to provide LLMs with additional information (including business-
specific data)- that is what RAG is for!
• Oracle - What Is Retrieval-Augmented Generation (RAG)?
• Oracle – What Is Generative AI? How Does It Work?

• RAG (Retrieval-Augment Generation) with Spring AI
• In a nutshell, RAG is a technique that:
• Reads unstructured data;
• Converts the data into tokens;
• It uses embeddings to transform the tokens into floating-point arrays
or vectors so that LLM can process and interpret them;
• It stores vectors in Vector Databases.
Retrieval-Augment
Generation (RAG)

RAG – Retrieval-Augment Generation
Spring AI with Open AI and Oracle Database 23ai
• Demo - RAG – Retrieval-Augmented
Generation with Spring AI +
OracleVectorStore + Oracle Database 23ai

Tool Calling - Spring AI
Source: Spring AI

Tool Calling - Spring AI with Open AI
• Demo - Tool Calling

Agentic Protocols – MCP, A2A, ACP

Introducing the Model Context Protocol (November 2024)
• MCP – Model Context Protocol (Anthropic)
• Standardizes context and tool integration between LLM hosts/clients and data/tool
servers
• Uses JSON-RPC 2.0 over standard transports (stdio and HTTP with SSE)
• Offers capability discovery and negotiation between client/browser and servers
• Multi-language SDK support (Python, TypeScript, Go, Java, C#, Rust)
• Focus: enabling LLMs to connect to external tools and data sources (“USB-C for AI”)

Announcing the Agent2Agent Protocol - A2A (April 2025)
• A2A – Agent2Agent Protocol (Google)
• Enables secure agent-to-agent communication and coordination in multi-agent
workflows
• Built on existing standards: HTTP, SSE, JSON-RPC, and security mechanisms.
• Uses “Agent Cards” (JSON contracts) for agent capability and trust discovery
• Facilitates cross-organizational and platform interoperability (Google, Oracle,
Microsoft, others)
• Focus: universal interoperability of autonomous agents across diverse environments

The simplest protocol for AI agents to work together (May 2025)
• ACP – Agent Communication Protocol (IBM)
• Defines a REST-based agent communication standard with well-defined HTTP
endpoints
• Supports multimodal message payloads using arbitrary MIME types (text, image,
audio, custom binary)
• Offers both synchronous and asynchronous (including streaming) modes
• No mandatory SDK: official SDKs available in Python and TypeScript
• Focus: broad agent-to-agent communication interoperability, agnostic of internal
architecture

Introducing the Model Context Protocol (Nov. 2024)

MCP – Architecture
Source: Model Context Protocol -
Introduction
• Client-server architecture – main components
• MCP Hosts: Programs like Claude Desktop, IDEs, or AI tools that want
to access data through MCP
• MCP Clients: Protocol clients that maintain 1:1 connections with
servers
• MCP Servers: Lightweight programs that each expose specific
capabilities through the standardized Model Context Protocol (1…*).
Servers can also include additional logic, caching, data
transformations, others
• Local Data Sources: Your computer’s files, databases, and services
that MCP servers can securely access
• Remote Services: External systems available over the internet (e.g.,
through APIs) that MCP servers can connect to
• MCP Inspector: debugging and analysis tool for inspecting,
visualizing, and validating context passed through the Model Context
Protocol in LLM applications

MCP –
Internals
Source: Demystifying
(MCP)

Spring AI + MCP Java SDK - Overview
Source: Spring AI MCP / MCP Java SDK

Spring AI – MCP Examples
• Demo – Model Context
Protocol (examples)

Spring AI + MCP – Dynamic Tool
Update
Source: Dynamic Tool Updates in Spring AI's Model Context Protocol
• Dynamic Tool Updates in Spring AI's Model Context
Protocol
• Zero Downtime Tooling
• Real-Time Discovery & Adaptability
• Future-Proof & Pluggable Architecture
• Unified, Structured Tool Interface
• Enhanced Agent-Style Orchestration

Input Prompt
Tools
Agent
Tool calling Tools results
Complement from tool results
User Interface
AI Agents
Agents Pipeline
AI
Final answer
Comprises Java tools/function
calling, MCP, API calls, etc...

• Workflows
• Systems where LLMs and tools are
orchestrated through predefined code
paths (prescriptive system)
• Agents
• Systems where LLMs dynamically direct
their own processes and tool usage
Spring AI - Agentic
Systems
Source: Spring AI

• Rationale
• While fully autonomous agents might seem appealing, workflows often provide
better predictability and consistency for well-defined tasks. This aligns perfectly
with enterprise requirements where reliability and maintainability are crucial.
• Fundamental Patterns
• Let's examine how we can implement such concepts through fundamental
design patterns, each serving specific use-case scenarios.
Spring AI - Agentic
Systems
Source: Spring AI

Chain Workflow
Source: Building Effective Agents with Spring AI

Parallelization Workflow

Routing Workflow

Orchestrator-Workers

Evaluator-Optimizer

Agentic Patterns – Best Practices
• Start Simple:
• Begin with basic workflows before adding complexity
• Use the simplest pattern that meets your requirements
• Add sophistication only when needed
• Design for Reliability:
• Implement clear error handling
• Use type-safe responses where possible
• Build in validation at each step
Consider Trade-offs:
• Balance latency vs. accuracy
• Evaluate when to use parallel processing
• Choose between fixed workflows and dynamic
agents

Reference
Architecture
for GenAI
Source: Architectural Approach for Building Generative AI Applications

Technical References
• Spring AI - https://spring.io/projects/spring-ai
• Spring AI - API - https://docs.spring.io/spring-ai/reference/api/index.html
• Getting started with Oracle’s contribution to Spring AI - https://rb.gy/estsp3
• Retrieval-Augmented Generation (RAG) with Spring AI, Oracle Database 23ai, and OpenAI
- https://rb.gy/e1mwt4
• Oracle Database 23ai https://rb.gy/pdmwmz
• Oracle AI Vector Search for Java Developers with the Oracle Database 23ai -
https://rb.gy/7ivwxr
• Oracle AI Vector Search User's Guide -
https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/preface.html

• Hibernate Vector Module - https://rb.gy/6h882g
• Getting started with Oracle’s contribution to LangChain4J - https://rb.gy/h21e1z
• LangChain4J - Retrieval-Augmented Generation (RAG) with LangChain4J and the
• Oracle Database 23ai https://rb.gy/pdmwmz
• Oracle AI Vector Search for Java Developers with the Oracle Database 23ai -
https://rb.gy/7ivwxr
• Oracle AI Vector Search User's Guide -
https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/preface.html

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Oracle Database for Java Developers

Java App
Dev with
Oracle
Database

Overview of Oracle DB Access with Java

Oracle JDBC - Support for the Latest Java
Versions
• Support for the latest Java and GraalVM
• Project Loom - Virtual Threads support
• Java Virtual Threads and Pipelined
Database Operations
• JDBC Standards - 4.2 and 4.3
• Reactive Streams - Java Flow API support
• R2DBC, Reactive Streams Ingestion (RSI)
• Pipelined Database Operations
Data access is critical in mission-
critical applications!

• Java / JDBC – https://www.oracle.com/jdbc
• Introduction to Oracle JDBC Driver Support for Virtual Threads - https://bit.ly/3UlNJWP
• Developing an Oracle JDBC app with GraalVM Native Image - https://rb.gy/iy3sgh
• Getting Started with Reactive Relational Database Connectivity and the Oracle R2DBC
Driver - https://rb.gy/42dnz5
• Getting Started with the Java library for Reactive Streams Ingestion (RSI) -
https://bit.ly/3rEiRnC
• Introduction to JDBC Reactive Extensions with the Oracle Database — Developer Release
– https://rb.gy/qxlrbx
• Pipelined Database Operations - https://rb.gy/iy3sgh

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