A
licenseNot graded
qualityD
maintenanceEnables LLM applications to generate, edit, describe, upscale, remix, reframe, and replace backgrounds in images using the Ideogram AI API.
10 npm
MIT
Scenario.com MCP Server
by pasie15
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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
ResponsivenessSyncing