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opencode-vision-mcp

opencode-vision-mcp

by avdivo
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

read_image

Analyzes an image to recognize text, objects, and scenes. Accepts a file path and optional question for specific information.

Instructions

Анализирует изображение: распознаёт текст, объекты, сцены. Принимает путь к файлу и опциональный вопрос.

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": {
      "file_path": "<file_path>"
    },
    "name": "read_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptNoВопрос или инструкция по картинке (по умолчанию: 'Опиши, что изображено на картинке')
file_pathYesАбсолютный путь к файлу изображения (.jpg, .png, .webp, .bmp)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description must cover behavioral traits. It mentions the tool analyzes images and recognizes text/objects/scenes, implying it is a read operation, but does not disclose error behavior, permissions, or output format. The description is adequate but not comprehensive.

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

Conciseness4/5

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

The description is two short sentences in Russian, conveying the core purpose efficiently without fluff. It is concise and front-loaded with the main action.

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?

With no output schema and simple parameters, the description covers the tool's function moderately well. However, it does not describe the return value or handling of errors, leaving some gaps for an AI agent to infer.

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 description adds minimal value beyond the schema. It reiterates that the tool takes a file path and optional question, but does not mention the default prompt value already in the schema. Baseline score of 3 is appropriate.

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 analyzes images and recognizes text, objects, and scenes. It specifies inputs (file path and optional question), making the purpose explicit and unambiguous.

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?

The description implies usage by mentioning inputs (file path and question) but provides no guidance on when to use this tool versus alternatives or any exclusions. Since no sibling tools exist, the lack of differentiation is acceptable, but explicit usage context is missing.

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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