A
licenseNot graded
qualityC
maintenanceEnables LLM applications to generate, edit, describe, upscale, remix, reframe, and replace backgrounds in images using the Ideogram AI API.
17 npm
MIT
Scenario.com MCP Server
by pasie15
Related Servers
Alternatives to Scenario.com MCP Server
No user-submitted related servers found.
Related Servers
- AlicenseBqualityAmaintenanceEnables AI image and video generation tasks (text-to-image, image-to-video, edit, upscale, etc.) via RunAPI, with support for polling and pricing lookups.10239 npmApache 2.0
Runway API MCP Serverofficial
AlicenseAqualityBmaintenanceEnables AI video and image generation through the Runway API. Supports video generation from images and text prompts, image creation, video upscaling and editing, and task management.711 npm22MIT
Runware MCP Serverofficial
AlicenseNot gradedqualityFmaintenanceEnables lightning fast image and video generation using the Runware API, with tools for inference, upscaling, background removal, and more.9MIT- AlicenseDqualityDmaintenanceEnables image generation, editing, and description using Ideogram AI's models through natural language.418 npm4MIT
- AlicenseNot gradedqualityDmaintenanceProvides 80+ image processing tools including AI generation, background removal, upscaling, local manipulation, and diagram rendering, all with built-in cost tracking and health monitoring.26 npmMIT
TDQS
C2.7/5.0
Scored across 107 tools
Disambiguation3/5
The tool set has clear distinctions between major resource types like assets, models, collections, and workflows, but there is significant overlap within inference tools (e.g., multiple ControlNet variants, img2img variants) that could confuse agents about which specific mode to use. Descriptions help differentiate, but the sheer number of similar-sounding inference endpoints creates ambiguity.
Naming Consistency5/5
Tool names follow a highly consistent verb_noun pattern throughout, using HTTP method prefixes (get, post, put, delete) followed by resource paths with hyphens. This uniform structure makes it predictable and easy to parse, even with the large number of tools.
Tool Count2/5
With 107 tools, the count is excessive for a single server, making it overwhelming and difficult for agents to navigate efficiently. While the domain (AI image generation and management) is broad, many tools could be consolidated (e.g., multiple inference variants) or handled via parameters rather than separate endpoints.
Completeness4/5
The tool surface provides comprehensive CRUD and lifecycle coverage for core resources (assets, models, collections, workflows) and includes a wide range of AI generation and editing operations. Minor gaps exist, such as limited model training management beyond cancel actions, but overall, the set supports most expected workflows in the domain.
Maintenance
ActivityInactive
ResponsivenessNo issues