NVIDIA NIM MCP Server Guide

This guide describes the custom Model Context Protocol (MCP) server configured to execute local tasks using the user's NVIDIA NIM API quota.

---

🗄️ File Locations

• MCP Server Implementation: [server.js](file:///C:/Users/kanna/nvidia-nim-mcp/server.js)
• MCP Config Registration: [mcp_config.json](file:///C:/Users/kanna/.gemini/antigravity-cli/mcp_config.json)
• MCP Local Schema Directory: [nvidia-nim/](file:///C:/Users/kanna/.gemini/antigravity-cli/mcp/nvidia-nim/)
  • [nim_run_file.json](file:///C:/Users/kanna/.gemini/antigravity-cli/mcp/nvidia-nim/nim_run_file.json)
  • [nim_chat.json](file:///C:/Users/kanna/.gemini/antigravity-cli/mcp/nvidia-nim/nim_chat.json)
  • [nim_list_models.json](file:///C:/Users/kanna/.gemini/antigravity-cli/mcp/nvidia-nim/nim_list_models.json)

---

🛠️ Available Tools & Schema

1. nim_run_file

Send a file's content to a NIM LLM with processing instructions, writing output directly to the filesystem.

• Arguments:
  • inputFile (string, required): Absolute path to the source code or text file.
  • outputFile (string, required): Absolute path where the processed result should be written.
  • instruction (string, required): Prompt telling the model what to do with the file content.
  • model (string, optional): Model ID to use. Defaults to meta/llama-3.3-70b-instruct.
  • systemPrompt (string, optional): Optional system prompt to instruct the model.

2. nim_chat

Send a direct prompt query to a NIM LLM model.

• Arguments:
  • prompt (string, required): Prompt string.
  • model (string, optional): Model ID. Defaults to meta/llama-3.3-70b-instruct.
  • systemPrompt (string, optional): Optional system prompt.

3. nim_list_models

Queries the NGC endpoint and returns all available model IDs.

• Arguments: None.

---

💡 Token-Saving Guidelines for Agents

To optimize token usage in our conversation window:

1. Do not pass raw file content to the model in standard prompts.
2. Instead, use nim_run_file and supply the inputFile and outputFile paths.
3. This offloads the entire payload extraction and output generation directly to the NIM API, keeping our chat history clean of huge text blocks.