Run EAP quantization cycle. Maps Ebbinghaus retention scores to embedding precision levels (32/8/4/2/0 bits). Downgrades low-retention embeddings to save storage; upgrades when retention improves. Run with dry_run=True first to preview changes. Args: profile_id: Profile to process (default: activ...
AI agents invoke quantize to trigger actions in Qualixar/superlocalmemory. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes a quantization cycle that actively modifies stored embeddings by changing their precision (32/8/4/2/0 bits). While it can be run in dry_run mode to preview, the actual execution modifies stored data. It's not purely destructive (data is downgraded/upgraded, not deleted) and not a simple write — it's a computational process that transforms stored memory representations.
From the tool's definition 'Run EAP quantization cycle' and 'Downgrades low-retention embeddings to save storage; upgrades when retention improves' — triggers a processing operation that modifies embedding precision levels in storage
Documented attack patterns abuse exactly the kind of access quantize gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Qualixar/superlocalmemory, and nothing reaches the server without passing your rules. This is the rule we recommend for quantize:
{
"version": "1",
"default": "deny",
"tools": {
"quantize": {
"limits": [
{
"counter": "quantize_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}quantize stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Run EAP quantization cycle. Maps Ebbinghaus retention scores to embedding precision levels (32/8/4/2/0 bits). Downgrades low-retention embeddings to save storage; upgrades when retention improves. Run with dry_run=True first to preview changes. Args: profile_id: Profile to process (default: active profile). dry_run: If True, compute stats but don't apply changes. It is categorised as a Execute tool in the Qualixar/superlocalmemory MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Qualixar/superlocalmemory MCP server in PolicyLayer and add a rule for quantize: 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 Qualixar/superlocalmemory. Nothing to install.
quantize is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the quantize 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 quantize. 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.
quantize is provided by the Qualixar/superlocalmemory MCP server (qualixar/superlocalmemory). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 59 Qualixar/superlocalmemory tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
59 Qualixar/superlocalmemory tools catalogued and risk-classified — across an index of 42,500+ MCP servers.