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ollama-vision-mcp

ollama-vision-mcp

by ducnm9
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

describe_image

Describe image content from a local file path. Supports brief, detailed, or exhaustive text summaries, enabling visionless LLM clients to interpret images via Ollama models.

Instructions

Describe the content of an image file.

Args: image_path: Absolute path to the image file (png, jpg, webp, gif) detail_level: Level of detail - "brief", "detailed", or "exhaustive"

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_path": "<image_path>"
    },
    "name": "describe_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
image_pathYes
detail_levelNodetailed

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It implies a read-only operation but doesn't explicitly state side-effect-free behavior or limitations. It does list acceptable file formats and detail levels, providing some transparency.

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 compact and well-structured: a clear one-sentence purpose followed by an Args block. Every line is informative, and the format is easy to scan.

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?

The description fully specifies the tool's inputs and purpose, which is sufficient for a straightforward 2-parameter read-only tool. Output semantics are handled by the output schema. It doesn't reference sibling tools, but that gap is captured in usage_guidelines.

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

Parameters4/5

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

Schema coverage is 0%, so the description compensates by explaining both parameters. image_path specifies absolute path and supported formats, and detail_level enumerates the allowed values. This adds meaningful context beyond the raw schema.

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 clearly states the tool's function: describing the content of an image file. The verb 'describe' and resource 'image file content' distinguish it from sibling tools like ocr_image or ask_image, though it doesn't explicitly name those alternatives.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus the sibling tools (ocr_image, ask_image, process_clipboard_image). The description only lists the arguments and their meanings, leaving the agent to infer usage context.

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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