sandbox_credentials_request
Run sandbox-credentials.request through the Appaloft application operation catalog. Shared with HTTP/API.
This record as markdown: /tools/appaloft-mcp/sandbox-credentials-request.md
What sandbox_credentials_request does on Appaloft
AI agents invoke sandbox_credentials_request to trigger actions in Appaloft. 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 |
|---|---|---|---|
url | string | Yes | |
method | string | Yes | |
grantId | string | Yes | |
headers | object | — | |
sandboxId | string | Yes | |
timeoutMs | integer | — | |
bodyBase64 | string | — |
Parameters from the server's own tool schema.
Why sandbox_credentials_request is rated High
sandbox_credentials_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.
Risk signalsAccepts URL/endpoint input (url)
Attacks that exploit this kind of access
The rule that runs sandbox_credentials_request safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Appaloft, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For sandbox_credentials_request, this is the rule to start with:
sandbox_credentials_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 Appaloft, apply this rule, and every sandbox_credentials_request call is checked against it from then on.
Questions about sandbox_credentials_request
Run sandbox-credentials.request through the Appaloft application operation catalog. Shared with HTTP/API. It is categorised as a Execute tool in the Appaloft MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
sandbox_credentials_request accepts 7 parameters: url, method, grantId, headers, sandboxId, timeoutMs, bodyBase64. Required: url, method, grantId, sandboxId. The full parameter table on this page comes from the server's own tool schema.
Register the Appaloft MCP server in PolicyLayer and add a rule for sandbox_credentials_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 Appaloft. Nothing to install.
sandbox_credentials_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 sandbox_credentials_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 sandbox_credentials_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.
sandbox_credentials_request is provided by the Appaloft MCP server (@appaloft/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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