orch_parse_airflow_dag
Parsea un archivo de Python que contiene un DAG de Airflow y extrae tareas y sus dependencias (AST).
This record as markdown: /tools/inggerman-mcps/orch-parse-airflow-dag.md
What orch_parse_airflow_dag does on MCP Framework Personal
AI agents invoke orch_parse_airflow_dag to trigger actions in MCP Framework Personal. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why orch_parse_airflow_dag is rated High
While the primary function appears to be read-only parsing and extraction, Airflow DAGs are executable Python code. The tool's ability to parse and potentially evaluate Python AST for a production Airflow DAG creates risk: (1) the parsing mechanism could execute initialization code in the DAG file, (2) malicious DAGs could be exploited, or (3) the extracted DAG could be subsequently executed by an agent.
From the tool's definition Tool parses a Python file containing an Airflow DAG and extracts tasks and dependencies using AST (Abstract Syntax Tree analysis).
Attacks that exploit this kind of access
The rule that runs orch_parse_airflow_dag safely
PolicyLayer is an MCP gateway: it sits between your AI agents and MCP Framework Personal, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For orch_parse_airflow_dag, this is the rule to start with:
orch_parse_airflow_dag stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect MCP Framework Personal, apply this rule, and every orch_parse_airflow_dag call is checked against it from then on.
Questions about orch_parse_airflow_dag
Parsea un archivo de Python que contiene un DAG de Airflow y extrae tareas y sus dependencias (AST). It is categorised as a Execute tool in the MCP Framework Personal MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the MCP Framework Personal MCP server in PolicyLayer and add a rule for orch_parse_airflow_dag: 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 Framework Personal. Nothing to install.
orch_parse_airflow_dag 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 orch_parse_airflow_dag 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 orch_parse_airflow_dag. 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.
orch_parse_airflow_dag is provided by the MCP Framework Personal MCP server (inggerman/mcps). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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