Report the result of a locally-executed pulled task.
AI agents use report_local_result to create or update resources in Hive — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Hive environment.
This tool writes/submits the result of a completed task back to the broker/cluster system. It creates or modifies data (task result records) in a reversible way — it doesn't delete anything, execute new code, or move money.
From the tool's definition Report the result of a locally-executed pulled task
Documented attack patterns abuse exactly the kind of access report_local_result gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Hive, and nothing reaches the server without passing your rules. This is the rule we recommend for report_local_result:
{
"version": "1",
"default": "deny",
"tools": {
"report_local_result": {
"limits": [
{
"counter": "report_local_result_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}report_local_result 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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Report the result of a locally-executed pulled task. It is categorised as a Write tool in the Hive MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Hive MCP server in PolicyLayer and add a rule for report_local_result: 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 Hive. Nothing to install.
report_local_result 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 report_local_result 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 report_local_result. 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.
report_local_result is provided by the Hive MCP server (saikodi/hive-compute-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Hive, 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.
12 Hive tools catalogued and risk-classified — across an index of 43,000+ MCP servers.