AI agents call pruva_search_documents to retrieve information from Pruva without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool queries and retrieves document search results with no side effects. It returns data (markdown list of matches) without creating, modifying, deleting, or executing anything.
From the tool's definition Tool name contains 'search' and description states it 'Search context documents by content within a product. Returns a markdown list of matches.' - a pure retrieval operation with no modification or execution of code.
Attacks that exploit this kind of access
Search context documents by content within a product. Returns a markdown list of matches. It is categorised as a Read tool in the Pruva MCP Server, which means it retrieves data without modifying state.
Register the Pruva MCP server in PolicyLayer and add a rule for pruva_search_documents: 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 Pruva. Nothing to install.
pruva_search_documents 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 pruva_search_documents 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 pruva_search_documents. 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.
pruva_search_documents is provided by the Pruva MCP server (pruva-ai/pruva-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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