Build a grounded RAG prompt from a question and retrieved context.
AI agents call build_rag_prompt to retrieve information from AI App MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool constructs a prompt string by combining a question with retrieved context. It reads/assembles data into a formatted prompt but does not persist anything, execute code, modify data, or trigger external operations. It is a pure data transformation/construction step in a RAG pipeline, making it a Read-category tool with low severity.
From the tool's definition Build a grounded RAG prompt from a question and retrieved context
Attacks that exploit this kind of access
Build a grounded RAG prompt from a question and retrieved context. It is categorised as a Read tool in the AI App MCP MCP Server, which means it retrieves data without modifying state.
Register the AI App MCP server in PolicyLayer and add a rule for build_rag_prompt: 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 AI App MCP. Nothing to install.
build_rag_prompt 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 build_rag_prompt 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 build_rag_prompt. 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.
build_rag_prompt is provided by the AI App MCP server (yh008/mcp). 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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