Vision MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| MAX_IMAGE_SIZE | No | Max image size in bytes | 10485760 |
| OPENROUTER_MODEL | No | AI model to use | anthropic/claude-3.5-sonnet |
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key |
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 | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| analyze_imageC | Analyze an image using AI vision models. Supports file paths and URLs. |
| list_modelsB | Get list of available AI vision models for vision analysis |
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 2 tools
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.
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.
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.
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.