MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual ...
Part of the InsideOut (Riley) server.
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AI agents invoke tfstatus to trigger processes or run actions in InsideOut (Riley). Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
tfstatus can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"tfstatus": {
"limits": [
{
"counter": "tfstatus_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full InsideOut (Riley) policy for all 24 tools.
These attack patterns abuse exactly the kind of access tfstatus gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). LIVENESS: The response carries two distinct timestamps: - updated_at — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - last_refresh_at — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.. It is categorised as a Execute tool in the InsideOut (Riley) MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the InsideOut (Riley) MCP server in PolicyLayer and add a rule for tfstatus: 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 InsideOut (Riley). Nothing to install.
tfstatus 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 tfstatus 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 tfstatus. 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.
tfstatus is provided by the InsideOut (Riley) MCP server (oci:docker.io/luthersystems/insideout-mcp:v0.36.3). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 24 InsideOut (Riley) tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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