Using Foundry Tools from Agents (MCP and Toolbox)

azd ai agent init --template intent-routing      # or exact-question-answering
azd up

import json
from azure.ai.projects import AIProjectClient
from azure.ai.projects.models import FunctionTool, PromptAgentDefinition
from azure.ai.textanalytics import TextAnalyticsClient
from azure.identity import DefaultAzureCredential

cred = DefaultAzureCredential()
project = AIProjectClient(
    endpoint="https://foundry-bookshelf.services.ai.azure.com/api/projects/proj-bookshelf", credential=cred
)
language = TextAnalyticsClient("https://foundry-bookshelf.cognitiveservices.azure.com/", cred)

def redact_pii(text: str) -> str:
    return language.recognize_pii_entities([text])[0].redacted_text

redact_tool = FunctionTool(
    name="redact_pii",
    description="Remove personal data from customer text before storing or quoting it",
    parameters={"type": "object", "properties": {"text": {"type": "string"}}, "required": ["text"]},
)

agent = project.agents.create_version(
    agent_name="bookshelf-support",
    definition=PromptAgentDefinition(
        model="gpt-4.1-mini",
        instructions="You help Bookshelf customers. Always redact customer text before quoting it.",
        tools=[redact_tool],
    ),
)

openai = project.get_openai_client()
response = openai.responses.create(
    input="Summarize: 'Jane Doe, jane@example.com, says order 4512 is late.'",
    extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
)
# Answer every tool call of a round in one request, and keep going until no calls remain
while calls := [item for item in response.output if item.type == "function_call"]:
    outputs = [
        {
            "type": "function_call_output",
            "call_id": call.call_id,
            "output": redact_pii(**json.loads(call.arguments)) if call.name == "redact_pii" else "unknown tool",
        }
        for call in calls
    ]
    response = openai.responses.create(
        previous_response_id=response.id,
        input=outputs,
        extra_body={"agent": {"name": agent.name, "type": "agent_reference"}},
    )
print(response.output_text)

using Azure.AI.Projects;
using Azure.Identity;

var project = new AIProjectClient(
    new Uri("https://foundry-bookshelf.services.ai.azure.com/api/projects/proj-bookshelf"),
    new DefaultAzureCredential());

// List the connections (catalog MCP servers, AI Search, storage) available to agents in this project
foreach (var connection in project.Connections.GetConnections())
    Console.WriteLine($"{connection.Name}: {connection.Type}");

import com.azure.ai.projects.AIProjectClientBuilder;
import com.azure.ai.projects.ConnectionsClient;
import com.azure.identity.DefaultAzureCredentialBuilder;

ConnectionsClient connections = new AIProjectClientBuilder()
    .endpoint("https://foundry-bookshelf.services.ai.azure.com/api/projects/proj-bookshelf")
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildConnectionsClient();

connections.listConnections().forEach(c -> System.out.println(c.getName() + ": " + c.getType()));

TOKEN=$(az account get-access-token --scope https://ai.azure.com/.default --query accessToken -o tsv)
curl -s "https://$FOUNDRY.services.ai.azure.com/api/projects/proj-bookshelf/connections?api-version=v1" \
  -H "Authorization: Bearer $TOKEN" | jq -r '.value[].name'

# In the folder where you ran azd ai agent init
azd down --purge --force

az role assignment delete --assignee "$PROJECT_PRINCIPAL" --role "Cognitive Services User" \
  --scope "$(az cognitiveservices account show --name "$FOUNDRY" --resource-group "$RG" --query id -o tsv)"