sandbox_apply_patch
Atomic hotfix in one call: apply N surgical edits, then optionally rebuild and restart the service. Edits run first and sequentially (a failing edit aborts before any build). If rebuild is given and the build exits non-zero, the restart is SKIPPED so your running daemon stays up. Returns { applie...
This record as markdown: /tools/cloud-easybits-easybits/sandbox-apply-patch.md
What sandbox_apply_patch does on EasyBits
AI agents invoke sandbox_apply_patch to trigger actions in EasyBits. 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 sandbox_apply_patch is rated High
Executes file edits, build commands, and service restarts on a live running system.
From the tool's definition apply N surgical edits, rebuild and restart the service
Attacks that exploit this kind of access
The rule that runs sandbox_apply_patch safely
PolicyLayer is an MCP gateway: it sits between your AI agents and EasyBits, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For sandbox_apply_patch, this is the rule to start with:
sandbox_apply_patch 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.
The button opens the PolicyLayer dashboard: create your workspace, connect EasyBits, apply this rule, and every sandbox_apply_patch call is checked against it from then on.
Questions about sandbox_apply_patch
Atomic hotfix in one call: apply N surgical edits, then optionally rebuild and restart the service. Edits run first and sequentially (a failing edit aborts before any build). If rebuild is given and the build exits non-zero, the restart is SKIPPED so your running daemon stays up. Returns { applied, buildOutput, buildExitCode, restarted, status }. Use this to ship a fix to a running box without juggling write+exec+restart yourself. It is categorised as a Execute tool in the EasyBits MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the EasyBits MCP server in PolicyLayer and add a rule for sandbox_apply_patch: 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 EasyBits. Nothing to install.
sandbox_apply_patch 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 sandbox_apply_patch 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 sandbox_apply_patch. 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.
sandbox_apply_patch is provided by the EasyBits MCP server (@easybits.cloud/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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