ai_chat
Chat with your local Llama model. Zero cost, fully private. Supports multi-turn conversation, system prompts, and .brain file loading. Example: ai_chat({ message:
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What ai_chat does on 0nmcp
AI agents invoke ai_chat to trigger actions in 0nmcp. 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 ai_chat is rated High
This tool executes a local LLM inference process, triggering external computation. It supports loading '.brain files' and system prompts, which means it can influence model behavior dynamically. The effects depend on the arguments passed (message, system prompt, loaded files), classifying it as Execute.
From the tool's definition 'Chat with your local Llama model' and 'Supports multi-turn conversation, system prompts, and .brain file loading'
Attacks that exploit this kind of access
The rule that runs ai_chat safely
PolicyLayer is an MCP gateway: it sits between your AI agents and 0nmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ai_chat, this is the rule to start with:
ai_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.
The button opens the PolicyLayer dashboard: create your workspace, connect 0nmcp, apply this rule, and every ai_chat call is checked against it from then on.
Questions about ai_chat
Chat with your local Llama model. Zero cost, fully private. Supports multi-turn conversation, system prompts, and .brain file loading. Example: ai_chat({ message:. It is categorised as a Execute tool in the 0nmcp MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the 0n MCP server in PolicyLayer and add a rule for ai_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 0nmcp. Nothing to install.
ai_chat 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 ai_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.
Set action: deny in the PolicyLayer policy for ai_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.
ai_chat is provided by the 0n MCP server (0nmcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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