recall_as_of
AI agents call recall_as_of to retrieve information from Memanto MCP without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
The tool appears to retrieve historical memory state at a specific point in time ('as_of'), which is a read-only query operation. No side effects or data modification is implied. The empty description lowers confidence slightly, but the naming pattern and sibling context strongly indicate this is a memory retrieval function, consistent with the Read category.
From the tool's definition Tool name 'recall_as_of' indicates a retrieval operation consistent with the server's stated purpose to 'recall' data.
Attacks that exploit this kind of access
recall_as_of. It is categorised as a Read tool in the Memanto MCP MCP Server, which means it retrieves data without modifying state.
Register the Memanto MCP server in PolicyLayer and add a rule for recall_as_of: 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 Memanto MCP. Nothing to install.
recall_as_of 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 recall_as_of 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 recall_as_of. 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.
recall_as_of is provided by the Memanto MCP server (moorcheh-ai/memanto). 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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