adrata_ai_tool_execute
Execute the same governed AI CRM ToolDispatcher path used by Adrata chat. Free reads run directly. Reads that can spend external data credits return a spend hold; execute one only with confirmSpend:true, a reason, and an idempotencyKey (reuse the same key on retry). The response includes the appr...
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What adrata_ai_tool_execute does on Adrata
AI agents invoke adrata_ai_tool_execute 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 adrata_ai_tool_execute is rated High
adrata_ai_tool_execute 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 adrata_ai_tool_execute 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 adrata_ai_tool_execute, this is the rule to start with:
adrata_ai_tool_execute 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 adrata_ai_tool_execute call is checked against it from then on.
Questions about adrata_ai_tool_execute
Execute the same governed AI CRM ToolDispatcher path used by Adrata chat. Free reads run directly. Reads that can spend external data credits return a spend hold; execute one only with confirmSpend:true, a reason, and an idempotencyKey (reuse the same key on retry). The response includes the approved ceiling and an exact provider-reported receipt where available. Writes use a two-step, server-bound confirmationToken flow; a client approved boolean can never authorize a write. A non-interactive machine/OAuth principal (every MCP client) can preview writes but cannot complete one. 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 adrata_ai_tool_execute: 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.
adrata_ai_tool_execute 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 adrata_ai_tool_execute 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 adrata_ai_tool_execute. 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.
adrata_ai_tool_execute 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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