A
licenseNot graded
qualityD
maintenanceAn MCP server for image generation across Google, OpenAI, and Reve providers with budget control, cost reporting, and URL-based output instead of base64.
34 npm
1
MIT
gcv-kie-mcp
by extreez
Related Servers
Alternatives to gcv-kie-mcp
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceUniversal MCP server for all Kie AI image generation and editing models, enabling text-to-image, image-to-image, and image editing via natural language.37 npm2MIT
- AlicenseAqualityDmaintenanceMCP server for AI-powered media generation: images, videos, audio, and upscaling using 99 AI models.6MIT
- AlicenseAqualityBmaintenanceMCP server for AI-powered image, audio, and video generation, enabling media creation directly from Claude, Cursor, and other MCP clients.1141 npmMIT
- AlicenseAqualityBmaintenanceMCP server for Pixmax API enabling generation of images, video, text, audio, and 3D across dozens of models like Midjourney, Kling, and ElevenLabs.10MIT
- AlicenseAqualityBmaintenanceAn MCP server providing access to 554+ generative AI models with spend safety guardrails, enabling text-to-image, image-to-video, and more through a simple 4-call workflow.91MIT
TDQS
A3.9/5.0
Scored across 12 tools
Disambiguation5/5
Each tool has a clearly distinct purpose: diagnostic check, balance lookup, catalog listing/detail/refresh/manual entry, cost estimation, generation, task status, waiting, file upload, and spend ledger. There is no overlap between tools; even the catalog sub-tools are differentiated by action (list, show, refresh, set).
Naming Consistency4/5
All tools share the kie_ prefix and use snake_case, which ensures readability. However, the pattern is not perfectly uniform: catalog tools use a noun_verb format (kie_catalog_list, kie_catalog_show), while others use verb_noun (kie_estimate, kie_generate) or plain nouns (kie_balance, kie_ledger). Minor deviations exist but the overall structure remains predictable.
Tool Count5/5
With 12 tools, the server is well-scoped for its purpose—covering the complete kie.ai generation lifecycle from readiness checks and catalog exploration to cost estimation, generation, and spend tracking. Each tool earns its place without unnecessary duplication or bloat.
Completeness4/5
The core workflow is fully covered: catalog lookup, cost estimation, generation, task waiting, and ledger review. The only notable gaps are the lack of a cancel/abort task operation and a list-all-tasks endpoint, but these may be outside the designed scope and can be worked around via the ledger and status tools.
Maintenance
ActivitySlowing
ResponsivenessNo issues