kie-mcp
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TDQS
Scored across 47 tools
Core generation tools (generate_image, generate_video, generate_music, generate_tts) are clearly separated by modality, but the audio cluster has substantial overlap: generate_sfx vs generate_sounds, generate_tts vs generate_gemini_tts vs generate_dialogue, and six Suno editing tools (extend_music, upload_extend_audio, cover_audio, add_vocals, add_instrumental, replace_section) that an agent could easily confuse. The descriptions do disambiguate most of these cases (e.g. 'for loop/BPM/key control use generate_sounds instead'), which keeps it above a 2, but selection still requires reading long guidelines.
The dominant pattern is verb_noun (generate_image, list_tasks, check_credits, extend_music, upload_file), with model-specific tools consistently prefixed (veo_*, grok_*, runway_*). A handful of deviations exist — audio_isolation, speech_to_text, seedream_layer_decompose, profile_brief use noun-first or noun_verb ordering — but overall the vocabulary is predictable and readable.
47 tools is heavy for a single server, and many of the additions are narrow Suno/experimental features (generate_mashup, boost_style, regenerate_voice_clone, get_timestamped_lyrics, generate_midi, create_music_video) that inflate the surface well beyond core generate/list/check workflows. The breadth of modalities justifies more than a dozen tools, but this exceeds what an agent can reliably navigate.
Coverage is unusually thorough across image, video, music, TTS, SFX, voice cloning, transcription, upscaling, extending, segmentation, uploads, credit checks, and spend reporting, with clear lifecycle chains (generate → check_task → download_result). Minor gaps remain: no task cancellation, no asset deletion/cleanup, and task listing appears scoped to the current session only.