mcpbeat Sign in

Azure AI Projects Java Agent Skill

| Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.

4k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
142 d ago
last touched
this folder, not the whole repository

Install

one command, takes just this skill from the repository
npx skills add https://github.com/microsoft/skills --skill azure-ai-projects-java

What comes with it

12 083 bytes besides the instruction
references/examples.md

The instruction itself

13 sections, as written by the author

Azure AI Projects SDK for Java

High-level SDK for Azure AI Foundry project management with access to connections, datasets, indexes, and evaluations.

Installation

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-projects</artifactId>
    <version>1.0.0-beta.1</version>
</dependency>

Environment Variables

PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project> # Required for project configuration
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

Authentication

import com.azure.ai.projects.AIProjectClientBuilder;
import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;

TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

AIProjectClientBuilder builder = new AIProjectClientBuilder()
    .endpoint(System.getenv("PROJECT_ENDPOINT"))
    .credential(credential);

Client Hierarchy

The SDK provides multiple sub-clients for different operations:

| Client | Purpose |

|--------|---------|

| ConnectionsClient | Enumerate connected Azure resources |

| DatasetsClient | Upload documents and manage datasets |

| DeploymentsClient | Enumerate AI model deployments |

| IndexesClient | Create and manage search indexes |

| EvaluationsClient | Run AI model evaluations |

| EvaluatorsClient | Manage evaluator configurations |

| SchedulesClient | Manage scheduled operations |

// Build sub-clients from builder
ConnectionsClient connectionsClient = builder.buildConnectionsClient();
DatasetsClient datasetsClient = builder.buildDatasetsClient();
DeploymentsClient deploymentsClient = builder.buildDeploymentsClient();
IndexesClient indexesClient = builder.buildIndexesClient();
EvaluationsClient evaluationsClient = builder.buildEvaluationsClient();

Core Operations

List Connections

import com.azure.ai.projects.models.Connection;
import com.azure.core.http.rest.PagedIterable;

PagedIterable<Connection> connections = connectionsClient.listConnections();
for (Connection connection : connections) {
    System.out.println("Name: " + connection.getName());
    System.out.println("Type: " + connection.getType());
    System.out.println("Credential Type: " + connection.getCredentials().getType());
}

List Indexes

indexesClient.listLatest().forEach(index -> {
    System.out.println("Index name: " + index.getName());
    System.out.println("Version: " + index.getVersion());
    System.out.println("Description: " + index.getDescription());
});

Create or Update Index

import com.azure.ai.projects.models.AzureAISearchIndex;
import com.azure.ai.projects.models.Index;

String indexName = "my-index";
String indexVersion = "1.0";
String searchConnectionName = System.getenv("AI_SEARCH_CONNECTION_NAME");
String searchIndexName = System.getenv("AI_SEARCH_INDEX_NAME");

Index index = indexesClient.createOrUpdate(
    indexName,
    indexVersion,
    new AzureAISearchIndex()
        .setConnectionName(searchConnectionName)
        .setIndexName(searchIndexName)
);

System.out.println("Created index: " + index.getName());

Access OpenAI Evaluations

The SDK exposes OpenAI's official SDK for evaluations:

import com.openai.services.EvalService;

EvalService evalService = evaluationsClient.getOpenAIClient();
// Use OpenAI evaluation APIs directly

Best Practices

  • Use DefaultAzureCredential for production authentication
  • Reuse client builder to create multiple sub-clients efficiently
  • Handle pagination when listing resources with PagedIterable
  • Use environment variables for connection names and configuration
  • Check connection types before accessing credentials

Error Handling

import com.azure.core.exception.HttpResponseException;
import com.azure.core.exception.ResourceNotFoundException;

try {
    Index index = indexesClient.get(indexName, version);
} catch (ResourceNotFoundException e) {
    System.err.println("Index not found: " + indexName);
} catch (HttpResponseException e) {
    System.err.println("Error: " + e.getResponse().getStatusCode());
}

| Resource | URL |

|----------|-----|

| Product Docs | https://learn.microsoft.com/azure/ai-studio/ |

| API Reference | https://learn.microsoft.com/rest/api/aifoundry/aiprojects/ |

| GitHub Source | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects |

| Samples | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects/src/samples |

Other skills for the same job

different authors, same section of the catalogue
Lamindb
by christophacham
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

22k tokens
Latchbio Integration
by christophacham
×3

Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.

12k tokens
Modal
by christophacham
×3

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.

17k tokens
Pyhealth
by christophacham
×3

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

22k tokens
Github Workflow Automation
by ComeOnOliver
×3

Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management

9k tokens
Lamindb
by ComeOnOliver
×3

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

41k tokens
Ml Pipeline Workflow
by ComeOnOliver
×3

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

5k tokens
Pyhealth
by ComeOnOliver
×3

Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).

39k tokens

How to use it

Copy the folder

Take microsoft/azure-ai-projects-java from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.