nvidia-nim-mcp

analyze_image

Provide an image URL and a question to get detailed analysis, captioning, or visual Q&A from NVIDIA NIM vision models.

Instructions

Analyze and describe images using NVIDIA NIM vision and multimodal models. Provide an image URL and a prompt/question to get detailed analysis, captioning, or visual Q&A.

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": {
      "image_url": "<image_url>",
      "prompt": "<prompt>"
    },
    "name": "analyze_image"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No	Vision/multimodal model ID (e.g., meta/llama-3.2-90b-vision-instruct, meta/llama-3.2-11b-vision-instruct, nvidia/neva-22b, microsoft/phi-3.5-vision-instruct)
`top_p`	No	Nucleus sampling parameter
`detail`	No	Image detail level for analysis	auto
`prompt`	Yes	Analysis prompt or question about the image (e.g., 'Describe this image', 'What objects are in this image?', 'Extract text from this image')
`image_url`	Yes	URL of the image to analyze
`max_tokens`	No	Maximum tokens in response
`temperature`	No	Sampling temperature
`system_prompt`	No	System prompt to guide the analysis

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.1Aug 14, 2026

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are present, so the description carries the full burden. It reveals that the tool depends on NVIDIA NIM models and expects a URL, but it does not mention auth requirements, network/API dependence, rate limits, public URL accessibility, or how the optional model parameter is defaulted. This is a meaningful gap for an externally backed tool.

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 concise sentences front-load the tool's purpose and key usage steps. 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.

Completeness3/5

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

The description gives enough core context for a simple image-analysis use case and mentions expected output types, but with no output schema and no annotations it does not cover model defaulting, call latency, or failure behavior. It is minimally viable for a schema-rich tool.

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?

Schema description coverage is 100%, so the input schema already explains each parameter. The description adds little beyond paraphrasing image_url and prompt (URL and question), and does not clarify model selection or default behavior.

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

Purpose4/5

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

The description uses a specific verb-resource pair ('Analyze and describe images') and names the technology (NVIDIA NIM vision and multimodal models). It does not explicitly contrast with sibling tools like multimodal_task, but the image-analysis focus is distinct from generation/completion siblings.

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

Usage Guidelines3/5

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

It provides direct instructions ('Provide an image URL and a prompt/question'), implying use for image analysis, captioning, or visual Q&A. However, it does not explain when to choose this tool over multimodal_task or other siblings, nor does it mention exclusions.

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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