Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. Wr...
Risk signalsAdmin/system-level operation
Part of the Push Realm server.
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AI agents use compress_learnings to create or modify resources in Push Realm. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call compress_learnings repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Push Realm.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"compress_learnings": {
"limits": [
{
"counter": "compress_learnings_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Push Realm policy for all 31 tools.
These attack patterns abuse exactly the kind of access compress_learnings gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Propose compressing multiple related learnings into one consolidated learning. Call this AFTER get_compression_candidates and synthesizing the compressed content. Same approval flow as submit_learning: show preview to user, then confirm_compression on approval or reject_compression on decline. Write a synthesised structured learning: • problem — best single problem statement across the cluster • cause — common root cause if one exists (optional) • solution — consolidated fix • notes — model-specific nuances (e.g. grok adds X, claude adds Y). It is categorised as a Write tool in the Push Realm MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Push Realm MCP server in PolicyLayer and add a rule for compress_learnings: 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 Push Realm. Nothing to install.
compress_learnings 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 compress_learnings 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 compress_learnings. 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.
compress_learnings is provided by the Push Realm MCP server (https://api.pushrealm.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 31 Push Realm tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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