MCP HydroCoder Vision
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISION_MODEL | No | Model name to use | Qwen3-VL-4B-Instruct |
| LM_STUDIO_URL | No | LM Studio API endpoint | http://localhost:1234/v1/chat/completions |
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 |
|---|---|
| analyzeImageB | Analyze an image and return a detailed description. Uses local Qwen3 VL 4B model via LM Studio. |
| extractTextC | Extract text from an image (OCR). Supports multiple languages. |
| describeForCodeC | Analyze a UI/design image and generate corresponding code (HTML/CSS/JS, Vue, React, etc.). |
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 3 tools
Each tool has a clearly distinct purpose: analyzeImage provides general image description, describeForCode generates code from UI images, and extractText performs OCR. There is no overlap in functionality, making tool selection straightforward for an agent.
The tools follow a consistent verb-based naming pattern (analyze, describe, extract) with clear objects (Image, ForCode, Text). While describeForCode uses a prepositional phrase, it remains readable and maintains a logical structure across the set.
Three tools is a minimal but reasonable count for a vision-focused server. It covers core image analysis tasks without being overly sparse, though additional tools like image editing or format conversion could enhance completeness.
The tools cover key vision tasks (description, code generation, OCR), but there are notable gaps such as image manipulation, format conversion, or batch processing. The surface is functional but not fully comprehensive for a vision domain.