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 Langfuse Observability without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
checkDeploy performs read-only operations by querying deployment status and retrieving logs from Hatchbox. It has no capability to modify, execute, or delete data—only to observe and report on existing deployment state. This is a classic Read operation with minimal blast radius if misused by an agent.
From the tool's definition Tool description states it 'retrieves real-time information' and 'Essential for monitoring deployments' with example response showing status queries and log retrieval with no modifications or side effects.
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 Langfuse Observability MCP Server, which means it retrieves data without modifying state.
Register the Langfuse Observability 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 Langfuse Observability. 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 Langfuse Observability MCP server (langfuse-observability-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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