Execute JavaScript in the page context and return the result.
AI agents invoke crow_browser_evaluate to trigger actions in Crow. 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.
This tool allows arbitrary JavaScript execution within a browser context. While the result is returned (not destructive by default), the ability to execute code means it can modify page state, steal data, trigger external calls, or perform actions dependent on the arguments passed. This is a classic Execute category risk, not Write (which is reversible) or Read (which has no side effects).
From the tool's definition Tool name and description: 'Execute JavaScript in the page context and return the result' — this directly invokes code execution.
Documented attack patterns abuse exactly the kind of access crow_browser_evaluate gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Crow, and nothing reaches the server without passing your rules. This is the rule we recommend for crow_browser_evaluate:
{
"version": "1",
"default": "deny",
"tools": {
"crow_browser_evaluate": {
"limits": [
{
"counter": "crow_browser_evaluate_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
} crow_browser_evaluate 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 JavaScript in the page context and return the result. It is categorised as a Execute tool in the Crow MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Crow MCP server in PolicyLayer and add a rule for crow_browser_evaluate: 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 Crow. Nothing to install.
crow_browser_evaluate 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 crow_browser_evaluate 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 crow_browser_evaluate. 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.
crow_browser_evaluate is provided by the Crow MCP server (kh0pper/crow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Crow, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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576 Crow tools catalogued and risk-classified — across an index of 43,000+ MCP servers.