NVIDIA NIM MCP Server
Provides tools to interact with the NVIDIA NIM API for running LLM inference on file contents or via chat prompts, and listing available models.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@NVIDIA NIM MCP ServerChat with the model about how to optimize SQL queries."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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
MCP Config Registration: mcp_config.json
MCP Local Schema Directory: nvidia-nim/
Related MCP server: ABSD DevOps MCP Server
🛠️ 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 tometa/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 tometa/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:
Do not pass raw file content to the model in standard prompts.
Instead, use
nim_run_fileand supply theinputFileandoutputFilepaths.This offloads the entire payload extraction and output generation directly to the NIM API, keeping our chat history clean of huge text blocks.
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