sklearn_add_evaluation

Generate model evaluation framework (Pro feature)

SERVERPlugin Hub SOURCE@barnburner121/plugin-hub-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/barnburner121-plugin-hub-mcp/sklearn-add-evaluation.md

WHAT IT DOES

What sklearn_add_evaluation does on Plugin Hub

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

RISK

Why sklearn_add_evaluation is rated Medium

An AI agent can call sklearn_add_evaluation faster than any human can review: one bad instruction and it creates or modifies resources in Plugin Hub by the hundred, each call as confident as the last.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs sklearn_add_evaluation safely

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

sklearn_add_evaluation Rate-limited

sklearn_add_evaluation 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": {
    "sklearn_add_evaluation": {
      "limits": [
        {
          "counter": "sklearn_add_evaluation_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 Plugin Hub, apply this rule, and every sklearn_add_evaluation call is checked against it from then on.

FAQ

Questions about sklearn_add_evaluation

What does the sklearn_add_evaluation tool do? +

Generate model evaluation framework (Pro feature). It is categorised as a Write tool in the Plugin Hub MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

How do I enforce a policy on sklearn_add_evaluation? +

Register the Plugin Hub MCP server in PolicyLayer and add a rule for sklearn_add_evaluation: 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 Plugin Hub. Nothing to install.

What risk level is sklearn_add_evaluation? +

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

Can I rate-limit sklearn_add_evaluation? +

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

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

sklearn_add_evaluation is provided by the Plugin Hub MCP server (@barnburner121/plugin-hub-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

KEEP EXPLORING

More on Plugin Hub, and thousands of servers like it.

This server

All 616 Plugin Hub tools →

Across the catalogue

add-tag on Kanban MCP Server →kb_add on Persistent Kb → The MCP Attack Database →

Guides

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