This record as markdown: /tools/io-github-kivanccakmak-yaver/supabase-deploy.md
What supabase_deploy does on Yaver
AI agents invoke supabase_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 |
|---|---|---|---|
function | string | — | Function name (deploys DB if empty) |
directory | string | — |
Parameters from the server's own tool schema.
Why supabase_deploy is rated High
Deploying to Supabase involves executing code or schema changes against a live database and function infrastructure. This is an Execute action because it runs operations whose effects depend on what is being deployed (the arguments/state). While deployment could have destructive consequences in edge cases, the primary classification is Execute since deployment itself is an orchestration of external operations.
From the tool's definition Tool name 'supabase_deploy' and description 'Deploy Supabase (db push or function deploy)' indicates execution of deployment operations that trigger external infrastructure changes.
Risk signalsAccepts file system path (directory)
Attacks that exploit this kind of access
The rule that runs supabase_deploy safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Yaver, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For supabase_deploy, this is the rule to start with:
supabase_deploy 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 Yaver, apply this rule, and every supabase_deploy call is checked against it from then on.
Questions about supabase_deploy
Deploy Supabase (db push or function deploy). 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.
supabase_deploy accepts 2 parameters: function, directory. 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 supabase_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.
supabase_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 supabase_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 supabase_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.
supabase_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.
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