learn_from_performance

Update personal brand model with new performance data

SERVERObsidian Elite RAG MCP Server SOURCEaegntic/aegntic-mcp

Medium RISK CLASS

Category Write

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/aegntic-aegntic-mcp/learn-from-performance.md

WHAT IT DOES

What learn_from_performance does on Obsidian Elite RAG MCP Server

AI agents use learn_from_performance to create or update resources in Obsidian Elite RAG MCP Server, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Obsidian Elite RAG MCP Server environment.

RISK

Why learn_from_performance is rated Medium

The tool updates (modifies) a personal brand model with new data, which is a write operation that creates or modifies data in the system. It doesn't appear to be destructive or financial. The severity is medium because updating a model with incorrect or adversarial data could corrupt the knowledge base or bias future AI-powered recommendations, but the effect is reversible in principle.

From the tool's definition Update personal brand model with new performance data

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs learn_from_performance safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Obsidian Elite RAG MCP Server, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For learn_from_performance, this is the rule to start with:

learn_from_performance Rate-limited

learn_from_performance 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": {
    "learn_from_performance": {
      "limits": [
        {
          "counter": "learn_from_performance_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}

LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Obsidian Elite RAG MCP Server, apply this rule, and every learn_from_performance call is checked against it from then on.

FAQ

Questions about learn_from_performance

What does the learn_from_performance tool do? +

Update personal brand model with new performance data. It is categorised as a Write tool in the Obsidian Elite RAG MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on learn_from_performance? +

Register the Obsidian Elite RAG MCP Server MCP server in PolicyLayer and add a rule for learn_from_performance: 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 Obsidian Elite RAG MCP Server. Nothing to install.

What risk level is learn_from_performance? +

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

Can I rate-limit learn_from_performance? +

Yes. Add a rate_limit block to the learn_from_performance 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 learn_from_performance completely? +

Set action: deny in the PolicyLayer policy for learn_from_performance. 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 learn_from_performance? +

learn_from_performance is provided by the Obsidian Elite RAG MCP Server MCP server (aegntic/aegntic-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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Govern customer-ops agents →Argument validation →Scoped token →