| Ecosystem | 0 | 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 14 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Exposes Xiaohongshu social platform capabilities as a set of 13 standardized MCP tools consumable by AI clients (Claude, Cursor, Gemini CLI, Cline, VSCode). The service implements the Model Context Protocol specification on a /mcp endpoint with streamable HTTP transport, translating MCP tool calls into internal service method invocations. Each tool is registered in mcp_server.go with JSON schema definitions and dispatched through mcp_handlers.go to the underlying XiaohongshuService layer.
Unique: Implements full MCP protocol stack in Go with dual interface design (MCP + REST API on same port 18060), allowing both MCP clients and direct HTTP consumers to invoke the same underlying service methods without code duplication. Uses go-rod/rod for browser automation rather than direct API calls because Xiaohongshu lacks a public API.
vs alternatives: First open-source MCP server for Xiaohongshu with 12k+ GitHub stars; competitors either use REST-only APIs or require proprietary integrations, whereas this exposes the full platform through standardized MCP tooling.
Implements a two-phase authentication system: xiaohongshu-login binary handles interactive QR code scanning via headless Chrome, persisting authenticated session cookies to cookies.json; the main xiaohongshu-mcp service reads these cookies on startup and injects them into every subsequent browser session opened via go-rod/rod. This approach bypasses the need for API credentials by reusing the user's authenticated browser context across all platform operations.
Unique: Separates authentication (xiaohongshu-login) from service operation (xiaohongshu-mcp) into two distinct binaries, allowing one-time interactive login followed by unattended service execution. Uses go-rod/rod for headless Chrome automation rather than Selenium or Puppeteer, providing tighter Go integration and lower memory overhead.
vs alternatives: Avoids credential storage entirely by leveraging browser session cookies; competitors using direct API calls require API keys or OAuth tokens, which introduce credential management overhead and security risk.
Manages headless Chrome browser instances through go-rod/rod, implementing session pooling to reuse browser contexts across multiple operations. The service opens a browser instance on startup, injects authenticated cookies into each session, and reuses the browser for subsequent tool invocations. Browser lifecycle is tied to the service lifecycle — the browser is closed when the service shuts down. This approach reduces startup latency compared to opening a new browser for each operation.
Unique: Uses go-rod/rod for browser automation with session pooling, reusing browser instances across multiple operations to reduce startup latency. Injects authenticated cookies into each session, maintaining authentication state without re-authenticating for each operation.
vs alternatives: Browser pooling reduces latency compared to spawning new browsers for each operation; go-rod/rod provides tighter Go integration and lower memory overhead compared to Selenium or Puppeteer.
Extracts post metadata, user information, and engagement metrics by parsing the Xiaohongshu DOM through go-rod/rod's element selection and text extraction APIs. The service uses CSS selectors and XPath queries to locate elements, extract text content, and construct structured data objects. This approach enables operation without reverse-engineering proprietary APIs, but is brittle to HTML structure changes.
Unique: Uses go-rod/rod for DOM parsing and element selection, providing a Go-native approach to web scraping without external dependencies like BeautifulSoup or Cheerio. Extracts structured data directly from the live Xiaohongshu web interface, enabling operation without API reverse-engineering.
vs alternatives: DOM-based extraction works against the live platform without API maintenance; competitors using outdated or reverse-engineered APIs may break when Xiaohongshu updates its backend.
Implements consistent error handling and response serialization across MCP and REST interfaces. The service layer returns structured error objects with error codes, messages, and optional context; mcp_handlers.go and handlers_api.go translate these into protocol-specific responses (MCP error format or HTTP status codes). This design ensures that clients receive consistent error information regardless of which interface they use.
Unique: Implements error handling at the service layer with protocol-agnostic error types, allowing mcp_handlers.go and handlers_api.go to translate errors into protocol-specific formats. This design ensures consistent error semantics across MCP and REST interfaces.
vs alternatives: Centralized error handling reduces code duplication and ensures consistency; competitors with separate error handling paths for each protocol may have inconsistent error messages or codes.
Implements a stateless HTTP server (using Gin framework) where each MCP or REST request opens a fresh browser page/tab within the pooled browser instance, executes the operation, and closes the page. This approach isolates state between requests, preventing cross-request contamination while reusing the browser instance for performance. The server maintains no per-request state — all context is passed through request parameters.
Unique: Implements per-request browser page isolation within a pooled browser instance, balancing performance (reusing browser) with isolation (fresh page per request). Stateless HTTP server design enables horizontal scaling without session affinity or distributed state management.
vs alternatives: Per-request page isolation prevents cross-request state leakage compared to competitors that reuse the same page across multiple requests; stateless design enables horizontal scaling without session management overhead.
Provides two distinct publishing tools: publish_content for text-based posts with optional image attachments, and publish_with_video for video content. Both tools operate through browser automation, constructing the Xiaohongshu post creation form via DOM manipulation and submitting it through the live web interface. The service handles image/video file uploads, caption composition, and hashtag injection before form submission.
Unique: Implements publish_content and publish_with_video as separate MCP tools with distinct parameter schemas, allowing AI clients to choose the appropriate tool based on content type. Uses DOM-based form construction and submission rather than API calls, enabling operation against the live Xiaohongshu web interface without reverse-engineering proprietary APIs.
vs alternatives: Supports both text and video publishing through a single service, whereas most Xiaohongshu automation tools focus only on text; browser automation approach works against the live platform without requiring API maintenance as Xiaohongshu's web UI evolves.
Implements get_feed tool that retrieves the authenticated user's Xiaohongshu feed with cursor-based pagination. The service navigates the feed DOM, extracts post metadata (title, author, engagement metrics, timestamps), and returns paginated results. Cursor tokens encode the position in the feed, enabling clients to request subsequent pages without re-fetching earlier content.
Unique: Uses cursor-based pagination (opaque tokens) rather than offset-based pagination, reducing the risk of duplicate or skipped results when the feed is updated between requests. Extracts feed data via DOM parsing rather than API calls, making it resilient to Xiaohongshu's lack of a public feed API.
vs alternatives: Cursor-based pagination is more robust than offset-based approaches for dynamic feeds; competitors using offset pagination risk returning duplicate posts if new content is inserted during pagination.
+6 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs xiaohongshu-mcp at 34/100. However, xiaohongshu-mcp offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities