Refresh an existing cache by re-fetching the source content and creating a new Gemini cache. Preserves the alias and optionally updates TTL or system instruction.
AI agents use context_refresh to create or update resources in Mnemo — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mnemo environment.
This tool re-fetches source content and creates a new Gemini cache, which is a reversible write/update operation. It modifies an existing cache entry by replacing its content while preserving the alias. It does not delete data irreversibly nor execute arbitrary code, placing it in the Write category.
From the tool's definition Refresh an existing cache by re-fetching the source content and creating a new Gemini cache. Preserves the alias and optionally updates TTL or system instruction.
Documented attack patterns abuse exactly the kind of access context_refresh gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Mnemo, and nothing reaches the server without passing your rules. This is the rule we recommend for context_refresh:
{
"version": "1",
"default": "deny",
"tools": {
"context_refresh": {
"limits": [
{
"counter": "context_refresh_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}context_refresh stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Refresh an existing cache by re-fetching the source content and creating a new Gemini cache. Preserves the alias and optionally updates TTL or system instruction. It is categorised as a Write tool in the Mnemo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Mnemo MCP server in PolicyLayer and add a rule for context_refresh: 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 Mnemo. Nothing to install.
context_refresh is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the context_refresh 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 context_refresh. 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.
context_refresh is provided by the Mnemo MCP server (logos-flux/mnemo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Mnemo, 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.
6 Mnemo tools catalogued and risk-classified — across an index of 43,000+ MCP servers.