AI agents invoke stream_query to trigger actions in MCP Vertica. 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.
Given the server context (SQL execution, Vertica database management) and the sibling tools (execute_query, copy_data), 'stream_query' almost certainly executes a SQL query and streams results. Empty description lowers confidence. Defaulting to Execute since query execution is the primary server function; could be Read if it only streams SELECT results, but arbitrary SQL execution is possible.
From the tool's definition Tool name 'stream_query' on a server described as providing 'SQL query execution and database management capabilities'; description is empty and uninformative.
Documented attack patterns abuse exactly the kind of access stream_query gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MCP Vertica, and nothing reaches the server without passing your rules. This is the rule we recommend for stream_query:
{
"version": "1",
"default": "deny",
"tools": {
"stream_query": {
"limits": [
{
"counter": "stream_query_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}stream_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.
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stream_query. It is categorised as a Execute tool in the MCP Vertica MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP Vertica MCP server in PolicyLayer and add a rule for stream_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 MCP Vertica. Nothing to install.
stream_query 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 stream_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.
Set action: deny in the PolicyLayer policy for stream_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.
stream_query is provided by the MCP Vertica MCP server (nolleh/mcp-vertica). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from MCP Vertica, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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6 MCP Vertica tools catalogued and risk-classified — across an index of 43,000+ MCP servers.