Curate a set of previously extracted stories so that only the decisions still worth keeping are persisted. When to use: - After running infer_history in story-only mode (rare — infer_history already chains this step automatically). - When you have a pre-existing set of stories you want to re-cura...
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AI agents invoke evolve_decisions to trigger processes or run actions in Kawa Code MCP. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
evolve_decisions can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"evolve_decisions": {
"limits": [
{
"counter": "evolve_decisions_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Kawa Code MCP policy for all 21 tools.
These attack patterns abuse exactly the kind of access evolve_decisions gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Curate a set of previously extracted stories so that only the decisions still worth keeping are persisted. When to use: - After running infer_history in story-only mode (rare — infer_history already chains this step automatically). - When you have a pre-existing set of stories you want to re-curate without re-running history extraction. Inputs: - stories: array of story objects from a previous infer_history run. - repoPath (optional): when provided, curated results are persisted as intents and decisions for the repo after curation finishes. - model (optional): Anthropic model used for the curation pass. Behavior: - Runs asynchronously — returns immediately with a started/pending status while progress is reported separately.. It is categorised as a Execute tool in the Kawa Code MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Kawa Code MCP server in PolicyLayer and add a rule for evolve_decisions: 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 Kawa Code MCP. Nothing to install.
evolve_decisions is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the evolve_decisions 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 evolve_decisions. 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.
evolve_decisions is provided by the Kawa Code MCP server (kawacode-ai/kawa.mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 21 Kawa Code MCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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