setup_data_refresh
Generate a complete backend script that queries a data warehouse and auto-refreshes a Deloc dashboard. Creates a ready-to-deploy Python project (main.py, requirements.txt, Dockerfile) with scheduling instructions. Supported data sources: Snowflake, Postgres, MySQL, or any HTTP/REST API. For BigQu...
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What setup_data_refresh does on Deloc
AI agents use setup_data_refresh to create or update resources in Deloc, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Deloc environment.
Why setup_data_refresh is rated Medium
This tool generates and creates project files (main.py, requirements.txt, Dockerfile) for a backend data refresh pipeline. It writes/creates artifacts rather than executing them directly or deleting anything. The output is a ready-to-deploy project, but deployment itself is a separate step.
From the tool's definition Generate a complete backend script... Creates a ready-to-deploy Python project (main.py, requirements.txt, Dockerfile) with scheduling instructions
Attacks that exploit this kind of access
The rule that runs setup_data_refresh safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Deloc, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For setup_data_refresh, this is the rule to start with:
setup_data_refresh stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Deloc, apply this rule, and every setup_data_refresh call is checked against it from then on.
Questions about setup_data_refresh
Generate a complete backend script that queries a data warehouse and auto-refreshes a Deloc dashboard. Creates a ready-to-deploy Python project (main.py, requirements.txt, Dockerfile) with scheduling instructions. Supported data sources: Snowflake, Postgres, MySQL, or any HTTP/REST API. For BigQuery, prefer connect_bigquery + create_dashboard_query instead — Deloc then runs the query daily itself, with nothing for the user to host or schedule. Use this tool for BigQuery only if the user explicitly wants to run their own pipeline. Use this AFTER deploying a dashboard that fetches a data file (e.g. fetch(. It is categorised as a Write tool in the Deloc MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Deloc MCP server in PolicyLayer and add a rule for setup_data_refresh: 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 Deloc. Nothing to install.
setup_data_refresh is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the setup_data_refresh 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 setup_data_refresh. 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.
setup_data_refresh is provided by the Deloc MCP server (delocdev/deloc-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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