Add a doc page about MCP
This commit is contained in:
Christian Tzolov
@@ -104,6 +104,7 @@
|
||||
* xref:api/multimodality.adoc[Multimodality]
|
||||
* xref:api/etl-pipeline.adoc[]
|
||||
* xref:api/testing.adoc[AI Model Evaluation]
|
||||
* xref:api/model-context-protocol.adoc[Model Context Protocol]
|
||||
|
||||
* Service Connections
|
||||
** xref:api/docker-compose.adoc[Docker Compose]
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
[[MCP]]
|
||||
= Model Context Protocol (MCP)
|
||||
|
||||
The link:https://modelcontextprotocol.io/introduction[Model Context Protocol (MCP)] is an open protocol that standardizes how applications provide context to Large Language Models (LLMs).
|
||||
MCP provides an unified way to connect AI models to different data sources and tools, making integration seamless and consistent.
|
||||
It helps you build agents and complex workflows on top of LLMs. LLMs frequently need to integrate with data and tools, and MCP provides:
|
||||
- A growing list of pre-built integrations that your LLM can directly plug into
|
||||
- The flexibility to switch between LLM providers and vendors
|
||||
|
||||
== Spring AI MCP
|
||||
|
||||
NOTE: Spring AI MCP is an experimental project and subject to change.
|
||||
|
||||
link:https://github.com/spring-projects-experimental/spring-ai-mcp[Spring AI MCP] is an experimental project that provides Java and Spring Framework integration for the Model Context Protocol.
|
||||
It enables Spring AI applications to interact with different data sources and tools, through a standardized interface, supporting both synchronous and asynchronous communication patterns.
|
||||
|
||||
image::https://github.com/spring-projects-experimental/spring-ai-mcp/blob/main/spring-ai-mcp-architecture.jpg?raw=true[SpringAIMCP, 800]
|
||||
|
||||
The Spring AI MCP implements a modular architecture with the following components:
|
||||
|
||||
- Spring AI Application: Uses Spring AI framework to build Generative AI applications that want to access data through MCP
|
||||
- Spring MCP Clients: Spring AI implementation of the MCP protocol that maintain 1:1 connections with servers
|
||||
- MCP Servers: Lightweight programs that each expose specific capabilities through the standardized Model Context Protocol
|
||||
- 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
|
||||
|
||||
The architecture supports a wide range of use cases, from simple file system access to complex multi-model AI interactions with database and internet connectivity.
|
||||
|
||||
== Getting Started
|
||||
|
||||
Add the SDK to your Maven project:
|
||||
|
||||
|
||||
[tabs]
|
||||
======
|
||||
Maven::
|
||||
+
|
||||
[source,xml,indent=0,subs="verbatim,quotes"]
|
||||
----
|
||||
<dependency>
|
||||
<groupId>org.springframework.experimental</groupId>
|
||||
<artifactId>spring-ai-mcp-spring</artifactId>
|
||||
<version>0.1.0</version>
|
||||
</dependency>
|
||||
----
|
||||
|
||||
Gradle::
|
||||
+
|
||||
[source,groovy,indent=0,subs="verbatim,quotes"]
|
||||
----
|
||||
dependencies {
|
||||
implementation 'org.springframework.experimental:spring-ai-mcp-spring:0.1.0'
|
||||
}
|
||||
----
|
||||
======
|
||||
|
||||
TIP: Refer to the xref:getting-started.adoc#repositories[Repositories] section to add the Spring Milestone Repository to your build file.
|
||||
|
||||
The latter builds on top of mcp-core to provide some useful Spring AI abstractions, such as `McpFunctionCallback`.
|
||||
|
||||
Now create an `McpClient` to regester the MCP server tools with your ChatClient and let the LLM call them:
|
||||
|
||||
[source,java]
|
||||
----
|
||||
// https://github.com/modelcontextprotocol/servers/tree/main/src/brave-search
|
||||
var stdioParams = ServerParameters.builder("npx")
|
||||
.args("-y", "@modelcontextprotocol/server-brave-search")
|
||||
.addEnvVar("BRAVE_API_KEY", System.getenv("BRAVE_API_KEY"))
|
||||
.build();
|
||||
|
||||
var mcpClient = McpClient.sync(new StdioServerTransport(stdioParams));
|
||||
|
||||
var init = mcpClient.initialize();
|
||||
|
||||
var chatClient = chatClientBuilder
|
||||
.defaultFunctions(mcpClient.listTools(null)
|
||||
.tools()
|
||||
.stream()
|
||||
.map(tool -> new McpFunctionCallback(mcpClient, tool))
|
||||
.toArray(McpFunctionCallback[]::new))
|
||||
.build();
|
||||
|
||||
String response = chatClient
|
||||
.prompt("Does Spring AI supports the Model Context Protocol? Please provide some references.")
|
||||
.call().content();
|
||||
----
|
||||
|
||||
|
||||
== Example Demos
|
||||
|
||||
Explore these MCP examples in the link:https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol[spring-ai-examples/model-context-protocol] repository:
|
||||
|
||||
- link:https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/sqlite/simple[SQLite Simple] - Demonstrates LLM integration with a database
|
||||
- link:https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/sqlite/chatbot[SQLite Chatbot] - Interactive chatbot with SQLite database interaction
|
||||
- https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/filesystem[Filesystem] - Enables LLM interaction with local filesystem folders and files
|
||||
- https://github.com/spring-projects/spring-ai-examples/tree/main/model-context-protocol/brave[Brave] - Enables natural language interactions with Brave Search, allowing you to perform internet searches.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user