lru_simulate
Simulate an LRU cache over a sequence of key accesses and report hit/miss rates.
This record as markdown: /tools/io-github-malamutemayhem-unclick-mcp-server/lru-simulate.md
What lru_simulate does on UnClick
AI agents invoke lru_simulate to trigger actions in UnClick. 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.
| Parameter | Type | Required | Description |
|---|---|---|---|
accesses | array | Yes | Sequence of key accesses |
capacity | number | — | Cache capacity (default 4) |
Parameters from the server's own tool schema.
Why lru_simulate is rated High
The tool executes a computational simulation (LRU cache behavior) over a sequence of inputs and returns derived results (hit/miss rates). It does not read external data stores, write/modify persistent data, or perform destructive/financial actions. 'Simulate' and 'report' indicate an in-memory execution of logic, placing it in the Execute category.
From the tool's definition 'Simulate an LRU cache over a sequence of key accesses' — runs a simulation process over provided input
Attacks that exploit this kind of access
The rule that runs lru_simulate safely
PolicyLayer is an MCP gateway: it sits between your AI agents and UnClick, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For lru_simulate, this is the rule to start with:
lru_simulate 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.
The button opens the PolicyLayer dashboard: create your workspace, connect UnClick, apply this rule, and every lru_simulate call is checked against it from then on.
Questions about lru_simulate
Simulate an LRU cache over a sequence of key accesses and report hit/miss rates. It is categorised as a Execute tool in the UnClick MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
lru_simulate accepts 2 parameters: accesses, capacity. Required: accesses. The full parameter table on this page comes from the server's own tool schema.
Register the UnClick MCP server in PolicyLayer and add a rule for lru_simulate: 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 UnClick. Nothing to install.
lru_simulate 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 lru_simulate 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 lru_simulate. 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.
lru_simulate is provided by the UnClick MCP server (@unclick/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
More on UnClick, and thousands of servers like it.
Across the catalogue