分析思维模型学习系统的总体状况
AI agents call analyze-learning-system to retrieve information from Tianji without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool performs analysis and assessment of a learning system's state, which is fundamentally a read operation that retrieves information about the system. No creation, modification, deletion, execution of arbitrary code, or financial operations are implied. The blast radius is minimal—misuse would only return incorrect or misleading analysis data, not cause irreversible harm or execute external operations.
From the tool's definition Tool name 'analyze-learning-system' and description 'analyzes the overall state of a thinking model learning system' indicate data retrieval and querying functionality.
Documented attack patterns abuse exactly the kind of access analyze-learning-system gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Tianji, and nothing reaches the server without passing your rules. This is the rule we recommend for analyze-learning-system:
{
"version": "1",
"default": "deny",
"tools": {
"analyze-learning-system": {}
}
}analyze-learning-system is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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分析思维模型学习系统的总体状况. It is categorised as a Read tool in the Tianji MCP Server, which means it retrieves data without modifying state.
Register the Tianji MCP server in PolicyLayer and add a rule for analyze-learning-system: 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 Tianji. Nothing to install.
analyze-learning-system 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 analyze-learning-system 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 analyze-learning-system. 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.
analyze-learning-system is provided by the Tianji MCP server (lanyijianke/thinking_models_mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Tianji, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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19 Tianji tools catalogued and risk-classified — across an index of 43,000+ MCP servers.