GLM-Vision MCP Server
Related Servers
Alternatives to GLM-Vision MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityAmaintenanceProvides vision capabilities to text-only models (like DeepSeek) via Zhipu free vision models, enabling image analysis, OCR, and image comparison through natural language.MIT
- AlicenseNot gradedqualityBmaintenanceEnables text-only LLMs to see images/videos via cloud vision models, offering vision chat, OCR, grounding, and media info tools with free GLM fallback.Apache 2.0
- AlicenseNot gradedqualityAmaintenanceGrants image-understanding capabilities to AI coding assistants that lack native vision by routing screenshots, error messages, UI mockups and documents through a single unified analysis tool. Supports multiple vision model providers such as GLM, DeepSeek-OCR, Qwen-VL, Doubao and Hunyuan, with automatic multi-crop, compression and retry handling for text-dense images.417 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding models without native vision to analyze images such as screenshots, error messages, UI layouts, and documents through a single image_understand tool. Supports multiple vision LLM providers including GLM-4.6V, DeepSeek-OCR, Qwen3-VL-Flash, Doubao-Seed-1.6, Hunyuan-Vision, and any OpenAI-compatible endpoint.417 npmMIT
- AlicenseAqualityDmaintenanceBridges a vision model to enable text-only models like DeepSeek to describe images, extract text, and compare images via MCP tools.523 npm9MIT
- AlicenseNot gradedqualityAmaintenanceGives AI coding models without native vision the ability to analyze images — screenshots, error messages, UI mockups, and documents — through a single unified image understanding tool. It routes requests to multiple vision model providers (GLM, DeepSeek-OCR, Qwen, Doubao, Hunyuan, or any OpenAI-compatible endpoint) with automatic cropping, compression, OCR-friendly handling, and retries.417 npmMIT
TDQS
Scored across 1 tool
With only one tool, there is no possibility of overlap or misselection between tools. The single 'vision' tool has an unambiguous purpose (image recognition returning structured description and OCR text).
A single tool named 'vision' is trivially consistent since there are no siblings to clash with. It is a noun rather than a verb_noun pattern, but it is clear and readable.
One tool is thin in absolute terms, but the server has a tightly scoped single purpose (image-to-text), so a single capability fully matches its scope. It is appropriately minimal rather than under-built for the stated domain.
The tool covers the core need of converting an image to text with an optional custom prompt, which satisfies the surface for a vision utility. Minor gaps exist (no batch/multi-image handling or URL input), but agents can work around these by calling per-image.