hooks_model-outcome
Record model routing outcome for learning Use when native Bash hooks (via Claude Code\
This record as markdown: /tools/ruflo/hooks-model-outcome.md
What hooks_model-outcome does on Ruflo
AI agents use hooks_model-outcome to create or update resources in Ruflo, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ruflo environment.
Why hooks_model-outcome is rated Medium
The tool records (writes) model routing outcomes, which modifies stored learning/training data. This is a Write operation as it creates or updates persistent records. It does not execute code, delete data, or involve financial transactions. Severity is medium because misuse could corrupt the swarm's self-learning routing decisions, degrading agent coordination across the system.
From the tool's definition 'Record model routing outcome for learning' — the tool writes/persists outcome data to support adaptive learning
Attacks that exploit this kind of access
The rule that runs hooks_model-outcome safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Ruflo, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For hooks_model-outcome, this is the rule to start with:
hooks_model-outcome 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Ruflo, apply this rule, and every hooks_model-outcome call is checked against it from then on.
Questions about hooks_model-outcome
Record model routing outcome for learning Use when native Bash hooks (via Claude Code\. It is categorised as a Write tool in the Ruflo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ruflo MCP server in PolicyLayer and add a rule for hooks_model-outcome: 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 Ruflo. Nothing to install.
hooks_model-outcome 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 hooks_model-outcome 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 hooks_model-outcome. 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.
hooks_model-outcome is provided by the Ruflo MCP server (ruvnet/ruflo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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