AI agents call eval_relevance to retrieve information from Multivon without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Based on the tool name and sibling tools, this appears to be an evaluation/scoring tool that reads and assesses relevance of outputs. Empty description lowers confidence. Sibling tools like eval_faithfulness, eval_bias, eval_context_recall suggest a pattern of read-only evaluation metrics. No evidence of write, execute, or destructive behavior.
From the tool's definition Tool name 'eval_relevance' and server description 'evaluation tools'; description is empty or uninformative.
Attacks that exploit this kind of access
eval_relevance. It is categorised as a Read tool in the Multivon MCP Server, which means it retrieves data without modifying state.
Register the Multivon MCP server in PolicyLayer and add a rule for eval_relevance: 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 Multivon. Nothing to install.
eval_relevance 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 eval_relevance 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 eval_relevance. 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.
eval_relevance is provided by the Multivon MCP server (multivon-ai/multivon-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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