Get model quotas for a specific Azure location. Args: subscription_id: The ID of the Azure subscription. This is string with the format xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx location: The Azure location to retrieve quotas for. Returns: list: Returns a list of quota usages. Usage: Call this when yo...
AI agents call get_model_quotas 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.
This tool queries and returns quota usage data for a specific Azure location without creating, modifying, deleting, or executing any operations. It is a straightforward read operation that retrieves existing configuration/status information from Azure, making it low severity with minimal blast radius if misused by an agent.
From the tool's definition Tool retrieves quota information ('Returns: list: Returns a list of quota usages') with no side effects. The description explicitly states 'Call this when you need to get information about available quota,' indicating a pure data retrieval operation.
Documented attack patterns abuse exactly the kind of access get_model_quotas gives an agent:
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 get_model_quotas:
{
"version": "1",
"default": "deny",
"tools": {
"get_model_quotas": {}
}
}get_model_quotas is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get model quotas for a specific Azure location. Args: subscription_id: The ID of the Azure subscription. This is string with the format xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx location: The Azure location to retrieve quotas for. Returns: list: Returns a list of quota usages. Usage: Call this when you need to get information about available quota. You should ensure that you use a valid subscription id. 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.
Register the Azure AI Agent Service MCP Server MCP server in PolicyLayer and add a rule for get_model_quotas: 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.
get_model_quotas is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the get_model_quotas 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.
Set action: deny in the PolicyLayer policy for get_model_quotas. 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.
get_model_quotas 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.
Start from Azure AI Agent Service MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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28 Azure AI Agent Service MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.