Deletes a repository, organization, or environment GitHub Actions secret.
AI agents call secret-delete to permanently remove resources in Python — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Deleting GitHub Actions secrets is an irreversible operation that destroys data and cannot be undone. Secrets are critical infrastructure credentials, and their deletion could break CI/CD pipelines, authentication flows, or deployment processes. This falls squarely into the Destructive category as the primary harm is data loss/removal.
From the tool's definition Tool name is 'secret-delete' and description explicitly states it 'Deletes a repository, organization, or environment GitHub Actions secret.' The verb 'Deletes' combined with the irreversible nature of removing secrets indicates this is a destructive…
Documented attack patterns abuse exactly the kind of access secret-delete gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Python, and nothing reaches the server without passing your rules. This is the rule we recommend for secret-delete:
{
"version": "1",
"default": "deny",
"hide": [
"secret-delete"
]
} secret-delete disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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Deletes a repository, organization, or environment GitHub Actions secret. It is categorised as a Destructive tool in the Python MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Python MCP server in PolicyLayer and add a rule for secret-delete: 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 Python. Nothing to install.
secret-delete is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the secret-delete 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 secret-delete. 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.
secret-delete is provided by the Python MCP server (Dave-London/Pare). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Python, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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202 Python tools catalogued and risk-classified — across an index of 43,000+ MCP servers.