Enhanced RAG query with self-thinking: automatically identifies Python object references (e.g., AutomationCondition.eager), follows them to retrieve additional documentation and source code with appropriate detail level. Shows complete thinking process. Use this for complex questions that may inv...
AI agents call query_rag_enhanced to retrieve information from RAG Document Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a read-only operation that searches and retrieves information from documents without modifying, creating, or deleting any data. The 'self-thinking' and 'reference following' features are analysis enhancements, not write or execute operations. Even though it processes complex queries, it remains a data retrieval tool with no side effects on the underlying document store or external systems.
From the tool's definition Tool performs 'semantic search and question-answering over uploaded documents' and 'retrieves additional documentation and source code'. The description contains only retrieval verbs: 'query', 'identifies', 'follows them to retrieve', 'shows'.
Attacks that exploit this kind of access
Enhanced RAG query with self-thinking: automatically identifies Python object references (e.g., AutomationCondition.eager), follows them to retrieve additional documentation and source code with appropriate detail level. Shows complete thinking process. Use this for complex questions that may involve multiple concepts or when you want comprehensive answers with automatic reference following. It is categorised as a Read tool in the RAG Document Server MCP Server, which means it retrieves data without modifying state.
Register the RAG Document Server MCP server in PolicyLayer and add a rule for query_rag_enhanced: 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 RAG Document Server. Nothing to install.
query_rag_enhanced 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 query_rag_enhanced 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 query_rag_enhanced. 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.
query_rag_enhanced is provided by the RAG Document Server MCP server (jaimeferj/mcp-rag-docs). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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