Vison-MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISIONAI_API_KEY | Yes | API authentication key | |
| VISIONAI_BASE_URL | Yes | OpenAI-compatible API endpoint | |
| VISIONAI_MODEL_NAME | No | Vision model to use | gpt-4o |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| image_analysisB | Analyze any image with a general vision model. Returns a detailed description of the image content, key elements, text, colors, layout, and context clues. |
| extract_text_from_screenshotA | Extract text from screenshots. Optimized for terminals, code editors, documents, and general content. Returns extracted text preserving original structure. |
| ui_to_artifactA | Convert UI screenshots into structured deliverables: production-ready code, image-generation prompts, technical specifications, or detailed descriptions. |
| diagnose_error_screenshotA | Analyze error screenshots (build errors, runtime errors, stack traces) and propose actionable fixes with root cause analysis. |
| understand_technical_diagramA | Interpret architecture diagrams, flowcharts, UML, ER, sequence, and system topology diagrams. Returns structured analysis of components, relationships, design patterns, and improvement suggestions. |
| analyze_data_visualizationA | Read charts, dashboards, and statistical visualizations to surface insights, trends, patterns, and anomalies with actionable recommendations. |
| ui_diff_checkA | Compare two UI screenshots — design vs implementation — to identify visual differences, layout drift, style inconsistencies, missing elements, and typography discrepancies. |
| video_analysisA | Inspect videos (local files ≤8MB, remote URLs) to describe scenes, detect events, and answer questions about visual moments. Supports MP4, MOV, M4V. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Each tool has a distinct purpose: analyzing data visualizations, diagnosing error screenshots, extracting text, general image analysis, UI diff checking, UI-to-artifact conversion, technical diagram interpretation, and video analysis. No two tools overlap in function.
Names use mixed conventions: some follow verb_noun (e.g., analyze_data_visualization, diagnose_error_screenshot), others are noun_noun or use abbreviations (e.g., image_analysis, ui_diff_check, ui_to_artifact). This inconsistency may cause confusion.
With 8 tools, the server covers a broad range of vision tasks without being overwhelming. The count is slightly on the lower side but still well-scoped for a vision-focused MCP.
The tool set covers major vision domains: data visualization, error diagnosis, text extraction, general analysis, UI comparison, code generation, technical diagrams, and video. Minor gaps like object detection or facial recognition are acceptable for a general-purpose server.