cherry-pick
Applies specific commits to the current branch. Returns structured data with applied commits, any conflicts, and new commit hash.
This record as markdown: /tools/io-github-dave-london-docker/cherry-pick.md
What cherry-pick does on Docker
AI agents invoke cherry-pick to trigger actions in Docker. 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 cherry-pick is rated High
Cherry-pick executes a git operation that modifies the repository's branch by applying commits. It can cause conflicts and permanently alters commit history on the current branch. This is an Execute-level operation (running a git command with real side effects), and misuse could corrupt branch history or introduce unintended code changes, warranting high severity.
From the tool's definition Applies specific commits to the current branch. Returns structured data with applied commits, any conflicts, and new commit hash.
Attacks that exploit this kind of access
The rule that runs cherry-pick safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Docker, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For cherry-pick, this is the rule to start with:
cherry-pick 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 Docker, apply this rule, and every cherry-pick call is checked against it from then on.
Questions about cherry-pick
Applies specific commits to the current branch. Returns structured data with applied commits, any conflicts, and new commit hash. It is categorised as a Execute tool in the Docker MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Docker MCP server in PolicyLayer and add a rule for cherry-pick: 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 Docker. Nothing to install.
cherry-pick 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 cherry-pick 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 cherry-pick. 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.
cherry-pick is provided by the Docker MCP server (@paretools/docker). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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