AI agents invoke execute_sql to trigger actions in Databricks 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.
SQL execution is a classic Execute-category risk: it triggers external operations (database queries) whose effects are argument-dependent. While the tool itself doesn't inherently delete or modify (which would be Destructive or Write), a SQL statement could perform any operation including SELECT, UPDATE, DELETE, or DROP depending on what the AI agent submits.
From the tool's definition Tool name 'execute_sql' and description 'Execute a SQL statement' indicate execution of arbitrary SQL code against Databricks warehouse.
Documented attack patterns abuse exactly the kind of access execute_sql gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Databricks MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for execute_sql:
{
"version": "1",
"default": "deny",
"tools": {
"execute_sql": {
"limits": [
{
"counter": "execute_sql_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}execute_sql 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.
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Execute a SQL statement. It is categorised as a Execute tool in the Databricks MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Databricks MCP Server MCP server in PolicyLayer and add a rule for execute_sql: 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 Databricks MCP Server. Nothing to install.
execute_sql is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the execute_sql 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 execute_sql. 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.
execute_sql is provided by the Databricks MCP Server MCP server (markov-kernel/databricks-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Databricks 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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38 Databricks MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.