从论文描述生成 PyTorch 代码框架、训练循环或配置文件
AI agents invoke code_generator to trigger actions in Scientific Paper Reading Assistant. 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.
This tool generates executable code (PyTorch frameworks and training loops) from paper descriptions. While the tool itself produces code artifacts (which could be classified as Write), the primary risk is that the generated code is intended to be executed. Generated training loops and configuration files could contain harmful logic if the model is manipulated.
From the tool's definition 生成 PyTorch 代码框架、训练循环或配置文件 (generates PyTorch code frameworks, training loops, or configuration files)
Documented attack patterns abuse exactly the kind of access code_generator gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Scientific Paper Reading Assistant, and nothing reaches the server without passing your rules. This is the rule we recommend for code_generator:
{
"version": "1",
"default": "deny",
"tools": {
"code_generator": {
"limits": [
{
"counter": "code_generator_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}code_generator 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.
从论文描述生成 PyTorch 代码框架、训练循环或配置文件. It is categorised as a Execute tool in the Scientific Paper Reading Assistant MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Scientific Paper Reading Assistant MCP server in PolicyLayer and add a rule for code_generator: 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 Scientific Paper Reading Assistant. Nothing to install.
code_generator 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 code_generator 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 code_generator. 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.
code_generator is provided by the Scientific Paper Reading Assistant MCP server (lxy-hqu/-mcp-for-paper-read-based-on-ai-ide). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Scientific Paper Reading Assistant, 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.
7 Scientific Paper Reading Assistant tools catalogued and risk-classified — across an index of 43,000+ MCP servers.