bilibili-mcp
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- AlicenseAqualityCmaintenanceEnables AI clients to query and download Bilibili video data, including metadata, streams, danmaku, subtitles, comments, search, hot lists, users, live rooms, and account read-only items through MCP tools.15MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to operate a user's own Bilibili account through MCP tools: scan-code login, browsing recommendations and rankings, watching videos with AI summaries, subtitles and frame overviews, plus commenting, @-mentioning, direct messaging, posting dynamics, and uploading videos. All operations return structured JSON with built-in rate limiting, audit logging, and safety guardrails.3,796 PyPI263MIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP-capable AI assistants to operate Bilibili end to end through 27 tools: QR-code login, video search and content collection (comments, subtitles, danmaku), publishing videos and dynamics with AI-content declarations and self-only visibility, plus rankings, analytics, and messaging interactions. It is a modified fork that fixes cookie handling and the submission zone table.MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI clients to search and open Bilibili videos, control a logical playback clock (play/pause/seek), read and post comments, like, coin, favorite, and fetch or export subtitle tracks through compact plain-text tool output. Write operations require a Bilibili login, which can be established by QR scan in an accompanying web console.MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to directly operate Bilibili, providing 27 tools for login, search, content publishing, data analysis, and interaction.159MIT
- AlicenseNot gradedqualityCmaintenanceEnables agents to search Bilibili, read video details and comment threads, and post comments with built-in confirmation, rate limiting, and length safeguards. Also fetches Douyin video metadata on a best-effort basis and can open Douyin links in the default browser.MIT
TDQS
Scored across 29 tools
Most tools target clearly different resources+actions: reads (video_info, user_info, video_comments), news sources (rank_list, popular_list, weekly_list, precious_list), and writes (like_video, coin_video, post_comment, send_message). The main risk is weekly_issues vs weekly_list, whose boundaries (episode metadata vs videos of an episode) could confuse an agent, plus the cluster of distinct-but-similar list sources.
Everything is snake_case, but conventions are mixed: some names are verb_noun (list_followings, list_followers, list_favorites, post_comment, follow_user), while many use noun+list or bare nouns (rank_list, popular_list, weekly_list, precious_list, notifications, messages, history, toview). It stays readable but is not a predictable single pattern.
29 tools is on the heavy side for any single server, but the B站 domain is genuinely broad (feeds, search, users, comments, frames, and seven distinct write actions). Each tool maps to a real endpoint, so it is defensible though borderline.
Coverage is strong across the video-lifecycle: discover (search/rank/popular/weekly), inspect (video_info, video_frames, video_transcript, video_comments, user_info), and act (like/coin/favorite/comment/dynamic/follow/message) with a preview-confirm safety pattern. Minor gaps remain (no thread-level comment retrieval beyond video_comments, no edit/delete of own content).