rag_query
AI agents call rag_query to retrieve information from MCP Math Calculator Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The 'rag_query' tool is part of a RAG pipeline where it performs retrieval operations against stored collections. Query operations are read-only—they retrieve and return data without modifying, deleting, or executing external operations. The absence of a formal description lowers confidence slightly, but the tool name and RAG pattern are strong indicators of read-only functionality.
From the tool's definition Tool name 'rag_query' indicates a query/retrieval operation typical of RAG (Retrieval-Augmented Generation) systems.
Attacks that exploit this kind of access
rag_query. It is categorised as a Read tool in the MCP Math Calculator Server MCP Server, which means it retrieves data without modifying state.
Register the MCP Math Calculator Server MCP server in PolicyLayer and add a rule for rag_query: 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 Math Calculator Server. Nothing to install.
rag_query 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 rag_query 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 rag_query. 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.
rag_query is provided by the MCP Math Calculator Server MCP server (r-yash/mcpserver). 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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