Which integrations are available for this server?

Provides image analysis using Google Gemini vision models (e.g., gemini-2.5-flash-001) through the Google Generative Language API. Allows image analysis via a custom OpenAI-compatible endpoint, including local models served by Ollama (e.g., qwen2.5-vl-72b-instruct). Provides image analysis using OpenAI vision models (e.g., gpt-4o, gpt-4o-mini) through the OpenAI API.

How do I use vision-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@vision-mcp Analyze the image at C:/Users/me/photo.png and describe its contents" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

vision-mcp

by menoxz

Overview Schema Related Servers Score Discussions

Python

Local

vision-mcp

Serveur MCP (Model Context Protocol) d'analyse d'images par modèles de vision LLM, multi-fournisseur.

Analysez une ou plusieurs images à travers une interface MCP unifiée, avec le fournisseur de votre choix : OpenCode Go, OpenAI, Anthropic Claude, Google Gemini, ou tout endpoint compatible OpenAI (Ollama, vLLM, local).

Fonctionnalités

4 outils MCP : analyze_image, analyze_images, list_providers, check_vision
5 fournisseurs : opencode, openai, anthropic, google, custom (compatible OpenAI)
Formats d'image : PNG, JPG/JPEG, WEBP, GIF, BMP (par chemin de fichier absolu)
Logs sur stderr (stdout réservé au protocole MCP)
Clés API via variables d'environnement uniquement — aucun secret en dur

Related MCP server: MCP Vision Server

Fournisseurs supportés

Provider	Variable API Key	Base URL par défaut	Modèles vision
opencode	`OPENCODE_API_KEY`	`https://opencode.ai/zen/go/v1`	`qwen3.6-plus`, `qwen3.6-plus-free`, `deepseek-v4-flash`, `deepseek-v4-pro`, `glm-5.1`
openai	`OPENAI_API_KEY`	`https://api.openai.com/v1`	`gpt-4o`, `gpt-4o-mini`, `gpt-4.5-preview`, `o1`, `o3-mini`
anthropic	`ANTHROPIC_API_KEY`	`https://api.anthropic.com/v1`	`claude-sonnet-4-20250514`, `claude-3-5-sonnet-20241022`, `claude-3-opus-20240229`, `claude-3-haiku-20240307`
google	`GOOGLE_API_KEY`	`https://generativelanguage.googleapis.com/v1beta`	`gemini-2.5-flash-001`, `gemini-2.5-pro-001`, `gemini-2.0-flash-001`, `gemini-2.0-flash-lite-001`
custom	`CUSTOM_API_KEY`	`http://localhost:11434/v1`	N'importe quel modèle compatible OpenAI (Ollama, vLLM, local)

Note : certains modèles sont texte uniquement (ex. deepseek-v4-flash, deepseek-v4-pro) et ne supportent pas la vision.

Prérequis

Python 3.10 ou supérieur
pip disponible

Installation

git clone https://github.com/menoxz/vision-mcp.git
cd vision-mcp

# Environnement virtuel (recommandé)
python -m venv .venv
# Windows
.venv\Scripts\activate
# Linux / macOS
source .venv/bin/activate

# Dépendances
pip install -r requirements.txt

Variables d'environnement

Variable	Obligatoire	Description
`OPENCODE_API_KEY`	pour le provider `opencode`	Clé API OpenCode Go
`OPENCODE_BASE_URL`	non	URL de base (défaut : `https://opencode.ai/zen/go/v1`)
`OPENCODE_MODEL`	non	Modèle par défaut (défaut : `qwen3.6-plus`)
`OPENAI_API_KEY`	pour le provider `openai`	Clé API OpenAI
`OPENAI_MODEL`	non	Modèle par défaut (défaut : `gpt-4o`)
`ANTHROPIC_API_KEY`	pour le provider `anthropic`	Clé API Anthropic
`ANTHROPIC_MODEL`	non	Modèle par défaut (défaut : `claude-sonnet-4-20250514`)
`GOOGLE_API_KEY`	pour le provider `google`	Clé API Google Gemini
`GOOGLE_MODEL`	non	Modèle par défaut (défaut : `gemini-2.5-flash-001`)
`CUSTOM_API_KEY`	pour le provider `custom`	Clé API de l'endpoint personnalisé
`CUSTOM_BASE_URL`	non	URL de base (défaut : `http://localhost:11434/v1`)
`CUSTOM_MODEL`	non	Modèle par défaut (défaut : `qwen2.5-vl-72b-instruct`)

Configuration MCP (opencode.json)

Ajoutez ce bloc dans la section "mcp" de votre opencode.json (CLI ou VSCode) :

{
  "mcp": {
    "vision-mcp": {
      "type": "local",
      "command": [
        "python",
        "C:/chemin/vers/vision-mcp/server.py"
      ],
      "environment": {
        "PYTHONIOENCODING": "utf-8",
        "OPENCODE_API_KEY": "sk-...",
        "OPENCODE_BASE_URL": "https://opencode.ai/zen/go/v1",
        "OPENCODE_MODEL": "qwen3.6-plus"
      },
      "enabled": true
    }
  }
}

Redémarrez ensuite opencode (ou /reload).

Outils exposés

Outil	Description
`analyze_image(image_path, prompt, provider, model)`	Analyse une image avec un prompt texte
`analyze_images(image_paths, prompt, provider, model)`	Analyse/comparaison de plusieurs images (min. 2)
`list_providers()`	Liste les fournisseurs et leur état de configuration
`check_vision(provider, model)`	Teste un fournisseur/modèle avec une image de test (cercle rouge)

Exemples

analyze_image(image_path="C:/screenshot.png",
              prompt="Décris l'interface utilisateur.",
              provider="opencode")

analyze_images(image_paths=["C:/img1.png", "C:/img2.png"],
               prompt="Compare ces deux designs.",
               provider="openai", model="gpt-4o")

list_providers()
check_vision(provider="opencode")

Dépannage

Problème	Solution
`No API key configured`	Définir la variable d'environnement `<PROVIDER>_API_KEY`
Timeout (120 s)	Image trop grande — redimensionner (max ~20 Mo)
400 Bad Request	Le modèle ne supporte pas la vision
Aucune sortie	Les logs sont sur stderr ; stdout est réservé au protocole MCP

Licence

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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vision-mcp

Fonctionnalités

Fournisseurs supportés

Prérequis

Installation

Variables d'environnement

Configuration MCP (opencode.json)

Outils exposés

Exemples

Dépannage

Licence

Maintenance

Resources

Looking for Admin?

Related MCP Servers

llm-vision-mcp

MCP Vision Server

z_ai_vision_mcp_server_clone

vision-mcp

Related MCP Connectors

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