A
licenseA
qualityB
maintenanceProvides AI vision analysis via OpenRouter, enabling image understanding from local paths or URLs. Supports OCR, error diagnosis, and chart/UI analysis using Gemini models.
1
8 npm
MIT
Vision MCP Server
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- FlicenseNot gradedqualityBmaintenanceEnables pure text LLMs to understand images by acting as a proxy to vision models via OpenAI-compatible APIs. Supports local files, URLs, and base64 inputs for image analysis.-
- AlicenseAqualityCmaintenanceEnables text-only LLMs to analyze images by sending files, URLs, or data URIs to a separate OpenAI-compatible vision model through a single MCP tool.1MIT
TDQS
B3.1/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have clearly distinct purposes: analyze_image performs image analysis using vision models, while list_models retrieves available models. There is no overlap or ambiguity between these functions.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern (analyze_image, list_models) with clear, descriptive names that align well with their functions. The naming is uniform and predictable.
Tool Count2/5
With only 2 tools, the server feels thin for a vision analysis domain. While it covers basic analysis and model listing, typical vision servers might include additional operations like batch processing, model details, or image preprocessing, making this count borderline minimal.
Completeness2/5
The tool surface is severely incomplete for vision analysis. It lacks essential operations such as getting model details, preprocessing images, batch analysis, or managing analysis results, which could lead to agent failures in complex workflows.
Maintenance
ActivityInactive
ResponsivenessNo issues