app_status
Build/run status of a container app deployed via publish_site with a Dockerfile. Poll until status is live or failed.
This record as markdown: /tools/ai-dataecho-mcp/app-status.md
What app_status does on Mcp
AI agents call app_status to retrieve information from Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
logs | boolean | — | Include build logs |
slug | string | Yes | Site slug |
Parameters from the server's own tool schema.
Why app_status is rated Low
app_status is a read-only operation that retrieves status information about an already-deployed container. It has no side effects, does not execute code or trigger deployments, and does not modify any data. The action is purely observational—checking the state of an existing resource. Severity is low because misuse would only reveal status information without enabling harmful actions.
From the tool's definition Tool description states 'Poll until status is `live` or `failed`', indicating it queries and retrieves the current build/run status of a deployed container app without modifying or executing anything.
Attacks that exploit this kind of access
The rule that runs app_status safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For app_status, this is the rule to start with:
app_status is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp, apply this rule, and every app_status call is checked against it from then on.
Questions about app_status
Build/run status of a container app deployed via publish_site with a Dockerfile. Poll until status is live or failed. It is categorised as a Read tool in the Mcp MCP Server, which means it retrieves data without modifying state.
app_status accepts 2 parameters: logs, slug. Required: slug. The full parameter table on this page comes from the server's own tool schema.
Register the MCP server in PolicyLayer and add a rule for app_status: 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 Mcp. Nothing to install.
app_status 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 app_status 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 app_status. 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.
app_status is provided by the MCP server (https://dataecho.ai/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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