A
licenseA
qualityB
maintenanceEnables image analysis, OCR, and text-to-image generation through OpenAI-compatible APIs. Supports local paths, URLs, or base64 images with configurable models and backup endpoints.
3
18 npm
MIT
MCP Vision Server
by LZMW
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TDQS
A3.9/5.0
Scored across 3 tools
Disambiguation5/5
The three tools have clearly distinct purposes with no overlap: analyze_image for general image analysis, chat_vision for conversational image Q&A, and get_status for server monitoring. Each tool serves a unique function in the vision analysis workflow, making it easy for an agent to select the right one.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (analyze_image, chat_vision, get_status) with clear, descriptive verbs. The naming is uniform and predictable across the entire toolset, making it easy to understand each tool's function at a glance.
Tool Count4/5
Three tools is appropriate for a vision server's core functionality, covering analysis, conversation, and status checking. However, the count feels slightly minimal—additional tools like batch processing or image preprocessing could enhance completeness, but the current set is well-scoped for basic operations.
Completeness4/5
The toolset covers essential vision tasks: analysis, iterative questioning, and server status. Minor gaps exist, such as lacking tools for image manipulation (e.g., resize, crop) or batch processing, but agents can work around these with the provided tools for most common use cases in image understanding.
Maintenance
ActivityInactive
ResponsivenessNo issues