query_table · VayuChat MCP: Risk & Policy

WHAT IT DOES

What query_table does on VayuChat MCP

AI agents invoke query_table to trigger actions in VayuChat MCP. 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 query_table needs a policy

While the primary intent is filtering/reading data, pandas query() evaluates string expressions that can execute arbitrary Python code depending on the pandas version and query content. This goes beyond a simple read operation and constitutes code execution. The blast radius is high because it could be leveraged to exfiltrate or manipulate data loaded into the server's in-memory context.

From the tool's definition 'Filter a table using pandas query syntax' — pandas query syntax executes arbitrary expressions against a DataFrame, which can include side-effecting Python expressions or be used to probe sensitive data

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

query_table Rate-limited

query_table 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": {
    "query_table": {
      "limits": [
        {
          "counter": "query_table_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 VayuChat MCP, so you can run agents against it safely.

FAQ

Questions about query_table

What does the query_table tool do? +

Filter a table using pandas query syntax. It is categorised as a Execute tool in the VayuChat MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on query_table? +

Register the VayuChat MCP server in PolicyLayer and add a rule for query_table: 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 VayuChat MCP. Nothing to install.

What risk level is query_table? +

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

Can I rate-limit query_table? +

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

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

query_table is provided by the VayuChat MCP server (nipunbatra/vayuchat-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.