google-flow-mcp
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- AlicenseCqualityBmaintenanceEnables AI agents to orchestrate Google Flow video and image generation, including multi-scene planning, silent background generation, credit tracking, and video stitching via MCP.25MIT

PixVerse MCPofficial
AlicenseNot gradedqualityFmaintenanceEnables video generation from text, images, and more through MCP-compatible apps like Claude and Cursor.52MIT- AlicenseBqualityBmaintenanceEnables AI agents to take a raw script all the way to a finished, downloadable short-drama .mp4, covering AI rewrite, character consistency, storyboards, frames, video shots, TTS voiceover, and final cut with quote-before-spend billing from any MCP client.1324,392 npmMIT
- AlicenseNot gradedqualityCmaintenanceExposes video generation and editing tools to MCP-capable clients, enabling text-to-video, image-to-video, video editing, reusable voice and character profiles, and task status queries through natural language.4MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI clients like Claude and ChatGPT to generate images and videos, animate images, create lip-synced videos, list TTS voices, and manage media via remote MCP tools.-
- AlicenseNot gradedqualityBmaintenanceAn agentic AI video editor for Claude. It enables editing real video through MCP: cutting, captioning, reframing, scoring, and exporting finished MP4s from actual footage.MIT
TDQS
Scored across 23 tools
The tools largely target distinct resources and actions: generation vs editing vs status vs media retrieval are clearly differentiated by descriptions. There is minor potential overlap between agent-driven generation (flow_agent_chat) and direct generation (flow_generate_video/image), but the agent interface is presented as distinct. Overall, an agent should be able to select the right tool without much confusion.
All tools use a consistent flow_ prefix and snake_case, with a clear verb_noun pattern for most tools (list_models, create_project, generate_video). Slight deviations like flow_status and flow_video_status (noun-first) and the agent tools (flow_agent_respond, flow_agent_chat) break the verb_noun pattern, but the naming remains predictable and readable.
23 tools is on the heavy side for a Flow client, covering many aspects of project, character, scene, media, and agent management. While each tool addresses a distinct need, the count falls in the borderline heavy range (16-25), which may increase cognitive load for agents. A slightly leaner surface would likely be more effective.
Core workflows for projects, characters, scenes, media, generation, and agent interaction are well covered. However, deletion operations (project, character, scene) and project update are missing, and flow_list_models only lists video models, leaving minor gaps that agents can likely work around.