run_saved_query · Azure Log Analytics MCP Server MCP

WHAT IT DOES

What run_saved_query does on Azure Log Analytics MCP Server

AI agents invoke run_saved_query to trigger actions in Azure Log Analytics MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why run_saved_query needs a policy

Executing queries against log analytics systems can trigger arbitrary data retrieval, potential system operations, or side effects depending on query content. While not inherently destructive like delete operations, query execution in a cloud analytics platform is an Execute action—it runs code (KQL) whose effects depend on the saved query's contents.

From the tool's definition Tool executes a saved query against Azure Log Analytics using KQL, as stated in description 'Execute saved query' and server context detailing 'executing queries' and KQL support.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

run_saved_query Rate-limited

run_saved_query stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "run_saved_query": {
      "limits": [
        {
          "counter": "run_saved_query_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Azure Log Analytics MCP Server, so you can run agents against it safely.

FAQ

Questions about run_saved_query

What does the run_saved_query tool do? +

Execute saved query. It is categorised as a Execute tool in the Azure Log Analytics MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_saved_query? +

Register the Azure Log Analytics MCP Server MCP server in PolicyLayer and add a rule for run_saved_query: 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 Log Analytics MCP Server. Nothing to install.

What risk level is run_saved_query? +

run_saved_query is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit run_saved_query? +

Yes. Add a rate_limit block to the run_saved_query 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 run_saved_query completely? +

Set action: deny in the PolicyLayer policy for run_saved_query. 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 run_saved_query? +

run_saved_query is provided by the Azure Log Analytics MCP Server MCP server (rasta26/azure_log_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.