Question

You built a FastMCP server exposing a get_running_config tool that returns the full running configuration of a core router (over 40,000 lines). When the conversational AI agent calls this tool to answer a question about a single OSPF area, the LLM begins truncating output, ignoring earlier device data, and producing incomplete answers. What is the BEST design change to the MCP tool to resolve this?

Accepted Answer

Redesign the tool to accept a filter/section parameter and return only the relevant configuration subset (e.g., the requested OSPF area) instead of the entire config

Answer

Increase the LLM temperature so the model considers more of the returned configuration when generating its answer

Answer

Add a retry decorator to the tool so it re-sends the full configuration whenever the model truncates its response

Answer

Store the full configuration in an MCP resource and instruct the agent to always load the entire resource before answering any question