nvidia-nim-mcp

chat_completion

Send multi-turn conversations to NVIDIA NIM language models to receive completions. Supports Llama, Mistral, Gemma, Qwen, and other open-source LLMs.

Instructions

Send a multi-turn conversation to a NVIDIA NIM language model and receive a completion. Supports all major open-source LLMs including Llama 3.1, Mistral, Gemma, Qwen, and more.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "messages": "<messages>"
    },
    "name": "chat_completion"
  }
}

Input Schema

Name	Required	Description
`seed`	No
`stop`	No
`model`	No	NIM model ID (optional, uses default if not set)
`top_p`	No
`messages`	Yes
`max_tokens`	No
`temperature`	No
`system_prompt`	No	System prompt to prepend

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.1Aug 14, 2026

TDQS

A3.5/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the behavioral disclosure burden. It only states the basic operation and lists supported models; it does not disclose response format, authentication requirements, token usage, or the need to include full conversation history for multi-turn context. This is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each earning its place. The first sentence encapsulates the core action; the second adds model compatibility. No redundancy, front-loaded with the primary purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 8 parameters, no output schema, and no annotations, the description leaves major gaps: no return value explanation, no guidance on message structure, and no relationship to sibling tools beyond the multi-turn hint. The description is insufficient for an agent to use the tool effectively without significant schema inference.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is low at 25% (only model and system_prompt described). The description does not compensate for the omitted parameter meanings, failing to explain essential parameters like messages, temperature, max_tokens, top_p, seed, or stop. It adds no semantic value beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Send a multi-turn conversation to a NVIDIA NIM language model and receive a completion.' It uses a specific verb (send), resource (conversation to NIM model), and outcome (completion). The mention of 'multi-turn' distinguishes it from sibling text_generation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

While no explicit alternatives are named, the description establishes a clear use case: multi-turn conversations. This implies it should be used when conversational context is needed, as opposed to single-turn tools. However, it lacks explicit when-not-to-use guidance or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server