request_provider_action_execution
Request a scoped provider action execution without registering every provider endpoint as a separate MCP tool. Defaults to dry-run and returns policy, scope, billing, risk, and approval requirements before any live request.
This record as markdown: /tools/adrata-adrata-mcp/request-provider-action-execution.md
What request_provider_action_execution does on Adrata
AI agents invoke request_provider_action_execution to trigger actions in Adrata. 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 request_provider_action_execution is rated High
request_provider_action_execution 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 signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
The rule that runs request_provider_action_execution safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Adrata, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For request_provider_action_execution, this is the rule to start with:
request_provider_action_execution 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 Adrata, apply this rule, and every request_provider_action_execution call is checked against it from then on.
Questions about request_provider_action_execution
Request a scoped provider action execution without registering every provider endpoint as a separate MCP tool. Defaults to dry-run and returns policy, scope, billing, risk, and approval requirements before any live request. It is categorised as a Execute tool in the Adrata MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Adrata MCP server in PolicyLayer and add a rule for request_provider_action_execution: 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 Adrata. Nothing to install.
request_provider_action_execution 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 request_provider_action_execution 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 request_provider_action_execution. 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.
request_provider_action_execution is provided by the Adrata MCP server (@adrata/adrata-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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