@xzxzzx/bilibili-mcp
MCP ServerFreeBilibili MCP tool for video and comment summarization
Capabilities5 decomposed
bilibili video metadata extraction and summarization
Medium confidenceExtracts video metadata (title, description, duration, upload date, creator info) from Bilibili video URLs and generates AI-powered summaries of video content. Uses Bilibili's public API endpoints to fetch video information and integrates with LLM providers (via MCP protocol) to produce concise summaries without requiring video download or transcoding.
Implements Bilibili-specific API integration as an MCP server, enabling LLM-native access to Chinese video platform data without custom HTTP client code. Uses MCP's tool-calling protocol to expose video extraction and summarization as composable capabilities within LLM workflows.
Provides native MCP integration for Bilibili (vs. generic web scraping tools), enabling seamless composition with other MCP tools in multi-step LLM agent workflows.
bilibili video subtitle/caption extraction and processing
Medium confidenceRetrieves subtitle tracks (if available) from Bilibili videos and processes them for analysis or summarization. Handles Bilibili's subtitle API format, supports multiple subtitle languages when available, and can feed subtitle text to downstream LLM processing for content understanding without requiring video transcoding or speech-to-text.
Exposes Bilibili's subtitle API as an MCP tool, handling platform-specific subtitle format parsing and multi-language track selection. Integrates directly with LLM context windows, allowing subtitle text to be processed without intermediate storage or format conversion.
Avoids video download overhead (vs. ffmpeg-based subtitle extraction) and handles Bilibili's proprietary subtitle format natively, making it faster for LLM-based workflows.
bilibili video comment aggregation and summarization
Medium confidenceFetches top-level and nested comments from Bilibili videos via the platform's comment API, aggregates them by relevance/engagement metrics, and generates AI-powered summaries of audience sentiment and key discussion points. Uses pagination to handle large comment sections and filters comments by score/timestamp to surface most relevant feedback.
Implements Bilibili comment API pagination and filtering as an MCP tool, enabling LLM-driven comment analysis without custom API client code. Handles Chinese language comment processing and integrates summarization directly into the MCP tool response.
Native Bilibili API integration (vs. web scraping) ensures reliability and compliance; MCP protocol enables composition with other tools in multi-step LLM workflows.
mcp-based tool composition for multi-step video analysis workflows
Medium confidenceExposes video extraction, subtitle retrieval, and comment aggregation as discrete MCP tools that can be composed by LLM agents into multi-step workflows. Uses MCP's tool-calling protocol to allow an LLM to orchestrate calls across multiple Bilibili capabilities (e.g., fetch video metadata → extract subtitles → summarize comments → generate final report) without requiring explicit workflow orchestration code.
Implements MCP server pattern with multiple tools exposed via a single stdio transport, allowing LLM agents to discover and call Bilibili capabilities dynamically. Uses MCP's schema-based tool definition to enable LLM reasoning about tool sequencing without hardcoded workflows.
MCP protocol enables tool composition at the LLM level (vs. imperative orchestration code), allowing agents to dynamically decide which tools to call and in what order based on task context.
bilibili api authentication and session management
Medium confidenceManages Bilibili API authentication, including optional session token handling for accessing restricted content or higher rate limits. Implements credential storage and refresh logic to maintain valid sessions across multiple tool calls without requiring manual re-authentication for each request.
Encapsulates Bilibili authentication within the MCP server, abstracting credential management from individual tool calls. Handles session lifecycle (login, refresh, expiration) transparently so LLM agents don't need to manage auth state.
Centralizes authentication logic in the MCP server (vs. requiring each tool to handle auth independently), reducing credential exposure and simplifying multi-tool workflows.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓Content researchers analyzing Chinese video platforms
- ✓LLM application developers building Bilibili integration workflows
- ✓Teams building multilingual content discovery systems
- ✓Accessibility researchers working with Chinese video content
- ✓LLM-based content analysis pipelines requiring transcript input
- ✓Multilingual content teams needing subtitle extraction at scale
- ✓Content creators analyzing audience feedback on Bilibili
- ✓Researchers studying Chinese social media discourse
Known Limitations
- ⚠Requires public video URLs — cannot access private or age-restricted content
- ⚠Summarization quality depends on upstream LLM provider capabilities
- ⚠No support for live stream metadata extraction
- ⚠Rate-limited by Bilibili API — high-volume extraction may require backoff logic
- ⚠Only works with videos that have subtitles uploaded by creator or platform
- ⚠Subtitle availability varies by video and region
Requirements
Input / Output
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Bilibili MCP tool for video and comment summarization
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