ingest_learning · VibeServe MCP: Risk & Policy

WHAT IT DOES

What ingest_learning does on VibeServe

AI agents use ingest_learning to create or update resources in VibeServe — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your VibeServe environment.

WHY IT NEEDS A POLICY

Why ingest_learning needs a policy

This tool creates or modifies wiki entries (Write category). The severity is medium because unauthorized writes to a wiki could inject false information, disrupt documentation, or pollute learning records, but the impact is reversible—entries can be corrected or removed. This is not Destructive because saves are not deletion operations; not Execute because it does not run arbitrary code; and not Financial.

From the tool's definition Tool description states 'Save pipeline results/learnings to the local wiki' — the verb 'Save' indicates data creation/modification without permanent deletion or irreversibility.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

ingest_learning Rate-limited

ingest_learning 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "ingest_learning": {
      "limits": [
        {
          "counter": "ingest_learning_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches VibeServe, so you can run agents against it safely.

FAQ

Questions about ingest_learning

What does the ingest_learning tool do? +

Save pipeline results/learnings to the local wiki. It is categorised as a Write tool in the VibeServe MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on ingest_learning? +

Register the VibeServe MCP server in PolicyLayer and add a rule for ingest_learning: 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 VibeServe. Nothing to install.

What risk level is ingest_learning? +

ingest_learning is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit ingest_learning? +

Yes. Add a rate_limit block to the ingest_learning 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.

How do I block ingest_learning completely? +

Set action: deny in the PolicyLayer policy for ingest_learning. 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.

What MCP server provides ingest_learning? +

ingest_learning is provided by the VibeServe MCP server (ncsound919/vibeserve). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.