Check the status and progress of a deployment activity on Hatchbox. This tool retrieves real-time information about a deployment including its current status, build logs, and any error messages. Essential for monitoring deployments and troubleshooting failures. Example response: ✅ Deployment Stat...
AI agents call checkDeploy to retrieve information from Pointsyeah without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool performs a passive query/retrieval of deployment status and logs without triggering any new deployments, modifications, or side effects. It is a monitoring/observability tool that returns informational output about existing infrastructure state, consistent with the Read category.
From the tool's definition Tool description states it 'retrieves real-time information' and 'monitoring deployments', with the example showing read-only output of status, logs, and error messages.
Attacks that exploit this kind of access
Check the status and progress of a deployment activity on Hatchbox. This tool retrieves real-time information about a deployment including its current status, build logs, and any error messages. Essential for monitoring deployments and troubleshooting failures. Example response: ✅ Deployment Status: completed Activity ID: 12345 Output: [2024-01-15 10:30:00] Pulling latest code from repository... [2024-01-15 10:30:15] Installing dependencies... [2024-01-15 10:31:00] Compiling assets... [2024-01-15 10:32:00] Running database migrations... [2024-01-15 10:32:30] Restarting application servers... [2024-01-15 10:33:00] Deployment completed successfully! Status meanings: - pending: Deployment queued but not started - running: Deployment in progress - completed/success: Deployment finished successfully - failed/error: Deployment encountered errors Use cases: - Monitoring deployment progress after triggering - Troubleshooting failed deployments with error logs - Verifying successful deployments before announcing changes - Checking if migrations ran successfully - Tracking deployment duration and performance - Debugging asset compilation or dependency issues Important notes: - Activity IDs are returned by the triggerDeploy tool - Status updates in real-time as deployment progresses - Full deployment logs are included in the output - Failed deployments include error details for debugging. It is categorised as a Read tool in the Pointsyeah MCP Server, which means it retrieves data without modifying state.
Register the Pointsyeah MCP server in PolicyLayer and add a rule for checkDeploy: 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 Pointsyeah. Nothing to install.
checkDeploy is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the checkDeploy 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 checkDeploy. 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.
checkDeploy is provided by the Pointsyeah MCP server (slack-workspace-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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