Get detailed information about a specific Dingo LLM. Retrieves information including the LLM's description, capabilities, and configuration parameters. Args: llm_name: The name of the LLM to get details for. Returns: A dictionary containing details about the LLM.
AI agents call get_llm_details to retrieve information from Dingo MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This is a pure data retrieval operation with no side effects. It queries existing LLM configuration and metadata without creating, modifying, deleting, or executing any operations. The blast radius of misuse is minimal—an agent could only obtain information about available LLMs, which poses no security or operational risk.
From the tool's definition Tool performs retrieval of information about a specific LLM with no modification capability: 'Retrieves information including the LLM's description, capabilities, and configuration parameters.' The args accept only a query parameter (llm_name) and the return…
Documented attack patterns abuse exactly the kind of access get_llm_details gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Dingo MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for get_llm_details:
{
"version": "1",
"default": "deny",
"tools": {
"get_llm_details": {}
}
}get_llm_details is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get detailed information about a specific Dingo LLM. Retrieves information including the LLM's description, capabilities, and configuration parameters. Args: llm_name: The name of the LLM to get details for. Returns: A dictionary containing details about the LLM. It is categorised as a Read tool in the Dingo MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Dingo MCP Server MCP server in PolicyLayer and add a rule for get_llm_details: 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 Dingo MCP Server. Nothing to install.
get_llm_details 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 get_llm_details 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 get_llm_details. 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.
get_llm_details is provided by the Dingo MCP Server MCP server (migoxlab/dingo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Dingo MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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6 Dingo MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.