nvidia-nim-mcp

compare_models

Compare 2-5 AI models side-by-side on licensing, hardware needs, benchmarks, capabilities, and use cases. Get a structured comparison table to inform model selection.

Instructions

Compare 2-5 models side-by-side across key decision factors: licensing, hardware requirements, benchmarks, capabilities, and use case tags. Returns structured comparison table.

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": {
      "model_ids": "<model_ids>"
    },
    "name": "compare_models"
  }
}

Input Schema

Name	Required	Description	Default
`model_ids`	Yes	Array of 2-5 model IDs to compare (e.g., ['nvidia/nemotron-3-ultra-550b-a55b', 'deepseek-ai/deepseek-v4-pro', 'moonshotai/kimi-k2.6'])

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.1Aug 14, 2026

TDQS

A4/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the output type ('structured comparison table') and decision factors, but does not mention potential behavior around invalid model IDs, performance, or error handling. This is acceptable for a read-only comparison tool but leaves some uncertainty.

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?

The description is two concise sentences, front-loaded with the core action and key factors. Every word earns its place with no redundancy or fluff.

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

Completeness4/5

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

For a tool with a single parameter and no output schema, the description covers the purpose, input range, and output structure. It could be slightly more explicit about what 'structured comparison table' entails, but the listed factors give a clear picture.

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

Parameters3/5

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

The schema description covers 100% of the parameter with a clear explanation and example. The description adds no new semantic information beyond the schema, only reinforcing the 2-5 count. It meets the baseline for high schema coverage.

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 uses a specific verb ('Compare') with a clear resource ('models') and scope (2-5), plus the exact decision factors considered. This distinctly separates it from sibling tools like get_model_info (single model) and list_models (listing).

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?

The description clearly implies the use case (comparison of multiple models across decision factors), but it does not explicitly state when NOT to use it or name alternative tools. Since the purpose is unambiguous, the absence of explicit exclusion is a minor gap.

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

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