Generate code snippets and guidance for wrapping changes with feature flags. ⚠️ CRITICAL: This tool enforces RUNTIME-CONTROLLABLE feature flags. You MUST place flag checks INSIDE execution paths (handlers, functions), NOT wrapping route registrations, middleware mounting, or controller registrati...
Part of the Unleash server.
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AI agents call wrap_change to retrieve information from Unleash without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though wrap_change only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"wrap_change": {}
}
}See the full Unleash policy for all 13 tools.
These attack patterns abuse exactly the kind of access wrap_change gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Generate code snippets and guidance for wrapping changes with feature flags. ⚠️ CRITICAL: This tool enforces RUNTIME-CONTROLLABLE feature flags. You MUST place flag checks INSIDE execution paths (handlers, functions), NOT wrapping route registrations, middleware mounting, or controller registration. This tool provides language-specific templates and instructions for protecting code changes with feature flags. It helps you: - Find existing feature flag patterns in your codebase - Match detected conventions (imports, method names, wrapping styles) - Generate appropriate code snippets for your language/framework - Follow Unleash SDK best practices - Ensure flags are runtime controllable (toggle without redeploy) Supported languages: - TypeScript/JavaScript (Node, React, Vue, Angular) - Python (FastAPI, Django, Flask) - Go - Ruby (Rails) - PHP - C# (.NET) - Java (Spring Boot) - Rust The tool uses a prompt-based approach: it provides detailed instructions for searching your codebase for existing patterns and matching their conventions. If no patterns are found, it provides sensible defaults based on Unleash SDK documentation. Usage: 1. Call this tool with the flag name after creating a flag 2. Follow the search instructions to find existing patterns 3. Use the recommended template or match detected patterns 4. Test your implementation Best suited for use after evaluate_change recommends a flag and create_flag creates it.. It is categorised as a Read tool in the Unleash MCP Server, which means it retrieves data without modifying state.
Register the Unleash MCP server in PolicyLayer and add a rule for wrap_change: 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 Unleash. Nothing to install.
wrap_change is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the wrap_change 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 wrap_change. 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.
wrap_change is provided by the Unleash MCP server (Unleash/unleash-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 13 Unleash tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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