adrata_api_request
Governed low-level Adrata API request. Read calls run directly except sensitive denied surfaces. Writes default to dry-run; a live write requires dryRun:false plus approved:true, a reason, and an idempotencyKey, and additionally requires the connection to hold the matching write:* scope (connect_...
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What adrata_api_request does on Adrata
AI agents invoke adrata_api_request 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_api_request is rated High
adrata_api_request 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.
Attacks that exploit this kind of access
The rule that runs adrata_api_request 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_api_request, this is the rule to start with:
adrata_api_request 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_api_request call is checked against it from then on.
Questions about adrata_api_request
Governed low-level Adrata API request. Read calls run directly except sensitive denied surfaces. Writes default to dry-run; a live write requires dryRun:false plus approved:true, a reason, and an idempotencyKey, and additionally requires the connection to hold the matching write:* scope (connect_workspace with writeAccess:true). Without that scope the API rejects the call with 403 insufficient_scope. Use only after a higher-level tool is not available. 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_api_request: 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_api_request 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_api_request 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_api_request. 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_api_request 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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