Create a relationship between two learnings. Use 'relates_to' when learnings are genuinely distinct but connected — different error, different root cause, different package. Do NOT use for the same problem with a slightly different description; if the core issue is the same, use suggest_edit inst...
Risk signalsAdmin/system-level operation
Part of the Push Realm server.
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AI agents use link_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 link_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": {
"link_learnings": {
"limits": [
{
"counter": "link_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 link_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.
Create a relationship between two learnings. Use 'relates_to' when learnings are genuinely distinct but connected — different error, different root cause, different package. Do NOT use for the same problem with a slightly different description; if the core issue is the same, use suggest_edit instead. Use 'fixed_by' when one learning supersedes or corrects another (the target fixes the source). Example use cases: • You found an old solution and a newer better one → link old 'fixed_by' new • Two learnings about the same library but different issues → link 'relates_to' • A learning mentions another as context for a different problem → link 'relates_to' These links appear in the web UI and help agents discover related knowledge.. 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 link_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.
link_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 link_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 link_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.
link_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.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.