list_deployments_from_azure_ai_services

WHAT IT DOES

What list_deployments_from_azure_ai_services does on Azure AI Agent Service MCP Server

AI agents call list_deployments_from_azure_ai_services to retrieve information from Azure AI Agent Service MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

THE RISK

Low Risk

Why list_deployments_from_azure_ai_services needs a policy

This tool exclusively retrieves and queries existing deployment information without modifying, deleting, or executing operations. It has no side effects beyond returning read-only data. The action is a standard list/query operation, placing it firmly in the Read category with low severity due to minimal blast radius if misused by an agent.

From the tool's definition Tool name: 'list_deployments_from_azure_ai_services'. Description: 'Retrieves a list of deployments from Azure AI Services' and 'provides an overview of the models and services that are currently deployed.' The function returns 'a list containing the details…

Documented attack patterns abuse exactly the kind of access list_deployments_from_azure_ai_services gives an agent:

POLICY

How to control list_deployments_from_azure_ai_services

PolicyLayer is an MCP gateway — it sits between your AI agents and Azure AI Agent Service MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for list_deployments_from_azure_ai_services:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "list_deployments_from_azure_ai_services": {}
  }
}

list_deployments_from_azure_ai_services is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Azure AI Agent Service MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

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EXPLORE

Related tools and policies

More Azure AI Agent Service MCP Server tools

Execute deploy_model_on_ai_services deploy_model_on_ai_services Execute execute_dynamic_swagger_action Execute a dynamically generated tool from the Swagger specification. Args: Execute run_agent_eval Run agent evaluation on agent data. Accepts both plain text and JSON strings. Paramet Execute run_text_eval run_text_eval Execute agent_query_and_evaluate Query an agent and evaluate its response in a single operation. Parameters: - age Execute query_default_agent Send a query to the default configured Azure AI Agent. Parameters: - query: Text Execute connect_agent Connect to a specific Azure AI Agent and run a query. Parameters: - agent_id: ID Write create_azure_ai_services_account create_azure_ai_services_account

All 28 Azure AI Agent Service MCP Server tools →

Read tools on other servers

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about list_deployments_from_azure_ai_services

What does the list_deployments_from_azure_ai_services tool do? +

Retrieves a list of deployments from Azure AI Services. This function is used when a user requests information about the available deployments in Azure AI Services. It provides an overview of the models and services that are currently deployed and available for use. Parameters: ctx (Context): The context of the current session, which includes metadata and session-specific information. Returns: list: A list containing the details of the deployments in Azure AI Services. The list will include information such as deployment names, descriptions, and possibly other metadata relevant to the deployed services. Usage: Use this function when a user wants to explore the available deployments in Azure AI Services. This can help users understand what models and services are currently operational and how they can be utilized. Notes: - The deployments listed may include various models and services that are part of Azure AI Services. - The list may change frequently as new deployments are added or existing ones are updated. It is categorised as a Read tool in the Azure AI Agent Service MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on list_deployments_from_azure_ai_services? +

Register the Azure AI Agent Service MCP Server MCP server in PolicyLayer and add a rule for list_deployments_from_azure_ai_services: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Azure AI Agent Service MCP Server. Nothing to install.

What risk level is list_deployments_from_azure_ai_services? +

list_deployments_from_azure_ai_services is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit list_deployments_from_azure_ai_services? +

Yes. Add a rate_limit block to the list_deployments_from_azure_ai_services rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block list_deployments_from_azure_ai_services completely? +

Set action: deny in the PolicyLayer policy for list_deployments_from_azure_ai_services. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides list_deployments_from_azure_ai_services? +

list_deployments_from_azure_ai_services is provided by the Azure AI Agent Service MCP Server MCP server (microsoft-foundry/mcp-foundry). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.