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Glama
joshsssn

mcp-vision-server

by joshsssn
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
VISION_MODELNoModel name the endpoint expects.llama3.2-vision
VISION_API_KEYNoBearer token for the endpoint.ollama
VISION_TIMEOUTNoRequest timeout in seconds.60
VISION_ENDPOINTNoOpenAI-compatible vision API URL.http://localhost:11434/v1/chat/completions
VISION_MAX_IMAGE_BYTESNoReject images larger than this (20 MB).20971520

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

CapabilityDetails
tools
{
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
describe_imageA

Pass a local image file to a vision-capable AI and get back a text description.

IMPORTANT: The image parameter MUST be a local absolute file path on disk. Do NOT pass URLs, base64 strings, or data URIs — always provide the full absolute path to an image file (e.g. "C:\Users\photos\cat.jpg" or "/home/user/images/pic.png"). Supported formats: PNG, JPG, JPEG, GIF, WEBP.

Parameters: image (str, required): Local absolute file path to the image. prompt (str, optional): Instruction for the vision model. Defaults to 'Describe this image in detail.'

Returns: Text description produced by the vision model.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of confusion or overlap. The tool's purpose is uniquely defined.

Naming Consistency5/5

The single tool follows a clear verb_noun pattern (describe_image), which is consistent and descriptive.

Tool Count3/5

A single tool is on the thin side, but reasonable for a narrowly scoped vision-description server. It feels minimal but not trivial.

Completeness5/5

The tool fully covers the server's stated purpose of describing images from local files, with no obvious missing operations for that domain.

Maintenance

ActivitySlowing
ResponsivenessNo issues

Resources