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Glama
Badribn0612

img-gen

by Badribn0612
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

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

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
generate_imageC

Create images based on the prompt

Args:
    prompt: A prompt to generate the image using image generation model

Returns:
    A list of MCP Content objects containing the generated image

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

B3.1/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool 'generate_image' has a clear, singular purpose that cannot be confused with any other tool in the set.

Naming Consistency5/5

The single tool name 'generate_image' follows a clear verb_noun pattern, and since there are no other tools, consistency is inherently perfect with no deviations or mixed conventions to evaluate.

Tool Count2/5

A single tool is too few for a server named 'img-gen', which suggests a broader image generation domain. This minimal set feels thin and incomplete, as it lacks operations like editing, upscaling, or managing generated images, which are common in such domains.

Completeness2/5

The tool surface is severely incomplete for an image generation server. While 'generate_image' covers creation, there are obvious gaps such as no tools for editing images, retrieving past generations, or handling variations, which limits agent workflows and leads to dead ends.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources