Get detailed information about an LLM's embedded prompt. In the new architecture, prompts are embedded directly in LLM classes. This function retrieves the embedded prompt details for the specified LLM. Args: llm_name: The name of the LLM to get prompt details for. Returns: A dictionary containin...
AI agents call get_prompt_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 tool performs a read-only query operation to fetch prompt details from an LLM configuration. There are no side effects, no data modification, no code execution, and no destructive operations. It is a simple information retrieval function that falls squarely within the Read category.
From the tool's definition Tool description explicitly states it 'retrieves' and 'Get detailed information' about an LLM's embedded prompt. It takes an llm_name argument and 'Returns: A dictionary containing details' with no mention of modifications, deletions, or side effects.
Documented attack patterns abuse exactly the kind of access get_prompt_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_prompt_details:
{
"version": "1",
"default": "deny",
"tools": {
"get_prompt_details": {}
}
}get_prompt_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 an LLM's embedded prompt. In the new architecture, prompts are embedded directly in LLM classes. This function retrieves the embedded prompt details for the specified LLM. Args: llm_name: The name of the LLM to get prompt details for. Returns: A dictionary containing details about the LLM's embedded prompt. 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_prompt_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_prompt_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_prompt_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_prompt_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_prompt_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.