Delete specific observations from entities in the knowledge graph
AI agents call delete_observations to permanently remove resources in Knowledge Graph Memory Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Deleting observations from a knowledge graph that serves as persistent memory for chat applications permanently removes stored user information. This action cannot be undone and constitutes data loss. While not as severe as a critical system wipe, it is destructive in nature and warrants 'high' severity because it irreversibly modifies the user's memory store, which is the core function of this MCP server.
From the tool's definition Tool name is 'delete_observations' with description 'Delete specific observations from entities in the knowledge graph' — the verb 'delete' indicates irreversible removal of data.
Documented attack patterns abuse exactly the kind of access delete_observations gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Knowledge Graph Memory Server, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_observations:
{
"version": "1",
"default": "deny",
"hide": [
"delete_observations"
]
} delete_observations disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
Free to start. No card required.
Delete specific observations from entities in the knowledge graph. It is categorised as a Destructive tool in the Knowledge Graph Memory Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Knowledge Graph Memory Server MCP server in PolicyLayer and add a rule for delete_observations: 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 Knowledge Graph Memory Server. Nothing to install.
delete_observations is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_observations 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 delete_observations. 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.
delete_observations is provided by the Knowledge Graph Memory Server MCP server (yodakeisuke/mcp-memory-domain-knowledge). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Knowledge Graph Memory Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
9 Knowledge Graph Memory Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.