ollama_chat · ML Lab MCP: Risk & Policy

WHAT IT DOES

What ollama_chat does on ML Lab MCP

AI agents invoke ollama_chat to trigger actions in ML Lab MCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.

WHY IT NEEDS A POLICY

Why ollama_chat needs a policy

Sending a chat message to an Ollama model triggers an external operation — it runs inference on a local or remote LLM, consuming compute resources. The effect depends entirely on the message content passed as an argument, which could include prompt injection or eliciting harmful outputs. While it doesn't directly modify data, it executes an external AI model, placing it in the Execute category.

From the tool's definition Send a chat message to an Ollama model

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

ollama_chat Rate-limited

ollama_chat stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "ollama_chat": {
      "limits": [
        {
          "counter": "ollama_chat_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

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

FAQ

Questions about ollama_chat

What does the ollama_chat tool do? +

Send a chat message to an Ollama model. It is categorised as a Execute tool in the ML Lab MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on ollama_chat? +

Register the ML Lab MCP server in PolicyLayer and add a rule for ollama_chat: 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 ML Lab MCP. Nothing to install.

What risk level is ollama_chat? +

ollama_chat is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit ollama_chat? +

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

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

ollama_chat is provided by the ML Lab MCP server (pushpullcommitpush/ml-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.