Executes a tool. This tool will be used to execute a self-written tool.
AI agents invoke execute_tool to trigger actions in Riza. 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.
The tool executes self-written tools via the Riza Code Interpreter API. This is a code execution capability whose effects depend on the arguments (which tool is executed and with what parameters). It does not merely read data, nor does it necessarily destroy data, but it can trigger external operations and run arbitrary code logic. This falls squarely into the Execute category.
From the tool's definition Tool name 'execute_tool' combined with description 'Executes a tool. This tool will be used to execute a self-written tool' indicates runtime execution of arbitrary code or tool definitions.
Documented attack patterns abuse exactly the kind of access execute_tool gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Riza, and nothing reaches the server without passing your rules. This is the rule we recommend for execute_tool:
{
"version": "1",
"default": "deny",
"tools": {
"execute_tool": {
"limits": [
{
"counter": "execute_tool_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}execute_tool 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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Executes a tool. This tool will be used to execute a self-written tool. It is categorised as a Execute tool in the Riza MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Riza MCP server in PolicyLayer and add a rule for execute_tool: 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 Riza. Nothing to install.
execute_tool 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_tool 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_tool. 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_tool is provided by the Riza MCP server (riza-io/riza-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Riza, 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 Riza tools catalogued and risk-classified — across an index of 43,000+ MCP servers.