运行实现（布局布线）。自动执行 reset_run → launch_runs → wait_on_run。
AI agents invoke run_implementation to trigger actions in Vivado. 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.
run_implementation executes a complex multi-step EDA workflow (reset, launch, wait on runs) that triggers actual hardware design processing in Xilinx Vivado. This is a command execution tool that initiates external operations with side effects that cannot be easily undone without re-running the entire flow.
From the tool's definition Tool description states it 'runs implementation (layout and routing)' and 'automatically executes reset_run → launch_runs → wait_on_run'.
Documented attack patterns abuse exactly the kind of access run_implementation gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Vivado, and nothing reaches the server without passing your rules. This is the rule we recommend for run_implementation:
{
"version": "1",
"default": "deny",
"tools": {
"run_implementation": {
"limits": [
{
"counter": "run_implementation_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}run_implementation 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.
Free to start. No card required.
运行实现（布局布线）。自动执行 reset_run → launch_runs → wait_on_run。. It is categorised as a Execute tool in the Vivado MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Vivado MCP server in PolicyLayer and add a rule for run_implementation: 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 Vivado. Nothing to install.
run_implementation 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 run_implementation 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 run_implementation. 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.
run_implementation is provided by the Vivado MCP server (mapleleavessssssss-wq/vivado-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Vivado, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
27 Vivado tools catalogued and risk-classified — across an index of 43,000+ MCP servers.