Run a deployment from the current yaver code target or from an explicitly selected repo/machine. Supports direct host deploys to TestFlight, Play internal testing, Convex, Cloudflare, or combined all, plus optional GitHub/GitLab CI fallback. machine=auto asks Yaver to choose the best machine for ...
AI agents invoke code_deploy to trigger actions in Yaver. 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
app | string | — | Optional explicit app/project name override |
tag | string | — | GitHub release tag for artifact upload mode |
file | string | — | Optional artifact path for CI upload/release mode |
branch | string | — | CI branch (default main) |
ci_repo | string | — | Optional CI repo identifier; auto-detected from git when omitted |
machine | string | — | Optional executor machine: local, auto, or a device id/name |
surface | string | — | |
targets | array | — | Optional explicit deploy target list; overrides surface |
workflow | string | — | GitHub Actions workflow filename when ci_provider=github |
device_id | string | — | Optional remote device ID |
repo_path | string | — | Optional explicit repo path on the selected machine |
distribute | boolean | — | When true, multi-target deploys may choose different machines per target |
Parameters from the server's own tool schema.
code_deploy triggers real processes with real consequences. An agent gone sideways doesn't fire it once — it starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Risk signalsAccepts file system path (file) · High parameter count (14 properties)
Attacks that exploit this kind of access
Run a deployment from the current yaver code target or from an explicitly selected repo/machine. Supports direct host deploys to TestFlight, Play internal testing, Convex, Cloudflare, or combined all, plus optional GitHub/GitLab CI fallback. machine=auto asks Yaver to choose the best machine for the target; distribute=true lets multi-target deploys fan out across different machines to reduce CI cost and load hot spots. It is categorised as a Execute tool in the Yaver MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
code_deploy accepts 12 parameters: app, tag, file, branch, ci_repo, machine, surface, targets, workflow, device_id, repo_path, distribute. The full parameter table on this page comes from the server's own tool schema.
Register the Yaver MCP server in PolicyLayer and add a rule for code_deploy: 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 Yaver. Nothing to install.
code_deploy 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 code_deploy 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 code_deploy. 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.
code_deploy is provided by the Yaver MCP server (yaver-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.