APIKO MCP
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- AlicenseBqualityDmaintenanceIntegrates MuAPI with Claude Code to enable image and video generation, editing, model discovery, and prediction tracking through natural language commands.9116 npm4MIT
- AlicenseBqualityBmaintenanceEnables AI agents to create Vidu image-to-video and reference-to-video generation tasks, poll asynchronous results, and check pricing across two model variants. Works from Claude Code, Codex, Cursor, VS Code, and other MCP-compatible hosts.5253 npmApache 2.0
- AlicenseNot gradedqualityDmaintenanceGenerate AI images and videos from Claude, Cursor or any MCP client: 48+ models on one account (Flux 2, Nano Banana 2, Seedream 5, Kling V3, Seedance 2.5, Veo 3.1), with the exact cost in credits returned on every call.1MIT
- AlicenseAqualityBmaintenanceConnects Claude Code to image generation models (OpenAI GPT Image 2, Google Nano Banana) for generating, editing, and converting images via natural language.638 npmMIT
- FlicenseNot gradedqualityCmaintenanceEnables Claude to generate images and video clips through kie.ai models, create videos from frames or reference images, check rendering job status, and manage credits.-
- AlicenseBqualityCmaintenanceProvides tools for discovering AI models, checking pricing, retrieving API documentation context, and generating integration examples; also supports paid async image/video tasks.1041 npmMIT
TDQS
Scored across 8 tools
Each tool targets a distinct step in the workflow: discovery (search_models vs get_model), quoting (estimate_cost), billable creation (generate_image vs generate_video), polling (get_task), asset upload, and balance. Descriptions explicitly fence off overlap, e.g. 'never call generate again to check progress' and 'use search_models before get_model'.
Uniform snake_case verb_noun pattern across all eight tools (check_balance, search_models, get_model, estimate_cost, generate_image, generate_video, get_task, upload_image). Verbs are used consistently with their intent (get for single reads, search for listing).
Eight tools is well-scoped for a media generation API: each maps to a necessary stage (discover, inspect, quote, generate, poll, upload, balance) with no redundant entries. Nothing feels padded or missing at the count level.
The surface covers the full paid-generation lifecycle including cost safety, asset upload, and async polling, and result links are returned. Minor gaps: no task listing or cancellation tool, and text/chat is deliberately out of scope (deferred to the OpenAI-compatible API), which agents can work around but leaves a small hole.