Connect to remote MCP safely
You authorize remote MCP with OAuth, client credentials, or enterprise policy while keeping application identity separate. URL validation and SSRF protections stay enabled.
Unit example (nearest native match)
See the idea in context
AxMCPClient mcp = new AxMCPClient(new AxMCPStreamableHTTPTransport(endpoint), Map.of("namespace", "inventory"));
AxGen program = new AxGen(Ax.s("request:string -> answer:string"), Map.of("mcp", mcp));
OpenAICompatibleClient llm = new OpenAICompatibleClient(Map.of(
"api_key", key, "model", "gpt-5.4-mini"));
try {
AxMCPClient.CatalogSnapshot catalog = mcp.inspectCatalog();
System.out.println(Json.stringify(Map.of(
"tools", catalog.tools().stream().map(tool -> tool.get("name")).toList(),
"resources", catalog.resources(),
"resourceTemplates", catalog.resourceTemplates())));
System.out.println(Json.stringify(program.forward(
llm, Map.of("request", "Reindex inventory."))));
} finally {
mcp.close();
}Run itIn your own project
// Gradle (build.gradle):
implementation 'dev.axllm:ax:24.0.15'
// Maven (pom.xml):
<dependency>
<groupId>dev.axllm</groupId>
<artifactId>ax</artifactId>
<version>24.0.15</version>
</dependency>
import dev.axllm.ax.Ax;
import java.util.Map;
public class QuickStart {
public static void main(String[] args) throws Exception {
var llm = Ax.ai("openai", Map.of("apiKey", System.getenv("OPENAI_API_KEY")));
var classify = Ax.ax("review:string -> sentiment:class \"positive, negative, neutral\"");
var result = classify.forward(llm, Map.of(
"review", "Useful and boring in the best way."
));
System.out.println("sentiment: " + result.get("sentiment"));
}
}Set OPENAI_APIKEY in your environment before running provider-backed code.
From a clone of the ax repo:
npm run example -- java src/examples/java/mcp/NativeMCPToolsExample.javaActive practice
Answer 2 in a row to learn this · attempt 1