Kolosal Vision MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| KOLOSAL_API_KEY | Yes | Your API key from Kolosal AI (https://kolosal.ai) |
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_imageA | Analyze an image using Kolosal Vision AI. Provide an image (URL or base64-encoded) and a description of what you want to know about it. The tool will return a detailed analysis based on your query. |
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 1 tool
With only one tool, there is no possibility of confusion or overlap. The tool's purpose is clear and distinct, so agents cannot misselect.
The single tool name 'analyze_image' follows a clear verb_noun pattern. Consistency is trivially maintained since there are no other tools to compare.
At only one tool, the server feels thin even though it serves a narrow purpose. It is on the borderline of the acceptable range, as a vision server might benefit from additional tools like model listing or batch analysis.
For the stated purpose of image analysis, the tool appears to cover the full lifecycle: it takes an image and query and returns analysis. There are no obvious gaps in the surface for this domain.