browser_eval

SERVERClaude Flow SOURCEclaude-flow

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-ruvnet-claude-flow/browser-eval.md

WHAT IT DOES

What browser_eval does on Claude Flow

AI agents invoke browser_eval to trigger actions in Claude Flow. 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.

RISK

Why browser_eval is rated High

browser_eval executes arbitrary JavaScript in a live browser context, which can trigger external operations, modify page state, steal cookies/credentials during login flows, and interact with third-party services. While not destructive or financial by itself, the capability to automate browser actions and manipulate DOM elements represents a high-severity Execute risk.

From the tool's definition Tool description explicitly states 'Execute JavaScript in page context' and mentions 'browser automation', 'login flows', and 'replay against DOM'. These are hallmarks of code execution with external side effects.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs browser_eval safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Claude Flow, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For browser_eval, this is the rule to start with:

browser_eval Rate-limited

browser_eval 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": {
    "browser_eval": {
      "limits": [
        {
          "counter": "browser_eval_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Claude Flow, apply this rule, and every browser_eval call is checked against it from then on.

FAQ

Questions about browser_eval

What does the browser_eval tool do? +

Execute JavaScript in page context Use when native WebFetch is wrong because you need real browser automation — JS-heavy SPA scraping, login flows with cookie reuse, replay against DOM-drifted versions, AIDefence PII gating before content reaches Claude. For static HTML pages, native WebFetch is faster and free. It is categorised as a Execute tool in the Claude Flow MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on browser_eval? +

Register the Claude Flow MCP server in PolicyLayer and add a rule for browser_eval: 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 Claude Flow. Nothing to install.

What risk level is browser_eval? +

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

Can I rate-limit browser_eval? +

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

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

browser_eval is provided by the Claude Flow MCP server (claude-flow). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

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