Microsoft Foundry (Azure AI Foundry) + Neo4j Integration

{
  "mcp_servers": {
    "neo4j": {
      "url": "https://your-neo4j-mcp-server.com/mcp",
      "auth": {
        "type": "oauth2",
        "authorization_url": "https://login.microsoftonline.com/{tenant}/oauth2/v2.0/authorize",
        "token_url": "https://login.microsoftonline.com/{tenant}/oauth2/v2.0/token",
        "client_id": "your-client-id",
        "client_secret": "your-client-secret",
        "scope": "api://your-app-id/.default"
      }
    }
  }
}

from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient
from neo4j import GraphDatabase
import os

# Azure AI Foundry setup
credential = DefaultAzureCredential()
project_client = AIProjectClient(
    endpoint=os.getenv("AZURE_AI_PROJECT_ENDPOINT"),
    credential=credential
)

# Neo4j setup
driver = GraphDatabase.driver(
    "neo4j+s://demo.neo4jlabs.com:7687",
    auth=("companies", "companies")
)

# Define Neo4j tools
def query_company(company_name: str) -> dict:
    """Query company information from Neo4j."""
    query = """
        MATCH (o:Organization {name: $company})
        RETURN o.name as name,
               [(o)-[:LOCATED_IN]->(loc:Location) | loc.name] as locations,
               [(o)-[:IN_INDUSTRY]->(ind:Industry) | ind.name] as industries,
               [(o)<-[:WORKS_FOR]-(p:Person) | {name: p.name, title: p.title}][..5] as leadership
        LIMIT 1
    """
    records, summary, keys = driver.execute_query(
        query,
        company=company_name,
        database_="companies"
    )
    return records[0].data() if records else {}

def search_news(company_name: str, query: str) -> list:
    """Search news articles about a company."""
    query_str = """
        MATCH (o:Organization {name: $company})<-[:MENTIONS]-(a:Article)
        RETURN a.title as title, a.date as date
        ORDER BY a.date DESC
        LIMIT 5
    """
    records, summary, keys = driver.execute_query(
        query_str,
        company=company_name,
        database_="companies"
    )
    return [r.data() for r in records]

# Create agent with tools
agent = project_client.agents.create_agent(
    model="gpt-4",
    name="investment_researcher",
    instructions="""You are an investment research analyst.
    Use the available tools to research companies and generate reports.""",
    tools=[
        {"type": "function", "function": query_company},
        {"type": "function", "function": search_news}
    ]
)

# Or use MCP server
mcp_tools = project_client.mcp.get_tools("neo4j")
agent = project_client.agents.create_agent(
    model="claude-3-5-sonnet-20241022",  # Foundry supports both Claude and GPT
    name="investment_researcher",
    tools=mcp_tools
)

# Execute research
thread = project_client.agents.create_thread()
message = project_client.agents.create_message(
    thread_id=thread.id,
    role="user",
    content="Research Google's recent activities and generate a report"
)

run = project_client.agents.create_run(
    thread_id=thread.id,
    agent_id=agent.id
)

# Wait for completion and get results
result = project_client.agents.get_run(thread.id, run.id)
messages = project_client.agents.list_messages(thread.id)

# Use Azure CLI credentials for local dev
from azure.identity import AzureCliCredential
credential = AzureCliCredential()