llm_get_model
Get the current preferred LLM model for an agent.
This record as markdown: /tools/ai-rokha-rokha/llm-get-model.md
What llm_get_model does on Rokha
AI agents call llm_get_model to retrieve information from Rokha without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
agent_name | string | Yes | Name of the agent to get model for (e.g., 'clawros', 'rokha-agent', 'moros') |
Parameters from the server's own tool schema.
Why llm_get_model is rated Low
Retrieves configuration data without modifying state or triggering external operations.
From the tool's definition Get the current preferred LLM model for an agent
Attacks that exploit this kind of access
The rule that runs llm_get_model safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Rokha, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For llm_get_model, this is the rule to start with:
llm_get_model is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Rokha, apply this rule, and every llm_get_model call is checked against it from then on.
Questions about llm_get_model
Get the current preferred LLM model for an agent. It is categorised as a Read tool in the Rokha MCP Server, which means it retrieves data without modifying state.
llm_get_model accepts 1 parameter: agent_name. Required: agent_name. The full parameter table on this page comes from the server's own tool schema.
Register the Rokha MCP server in PolicyLayer and add a rule for llm_get_model: 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 Rokha. Nothing to install.
llm_get_model is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the llm_get_model 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 llm_get_model. 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.
llm_get_model is provided by the Rokha MCP server (https://rokha.ai/mcp/jsonrpc). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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