Weibo MCP Server

Overview

The Weiboresou MCP Server is a lightweight, protocol‑driven service that fetches the top N trending Weibo posts and exposes them to AI assistants via the Model Context Protocol (MCP). By turning a popular social‑media feed into an AI‑friendly data source, the server solves a common pain point for developers who need real‑time insights into Chinese social trends without writing custom web scrapers or dealing with API rate limits.

The server operates in two transport modes that cater to different deployment scenarios. In stdio mode, the MCP client runs a local Python script that directly queries Weibo and streams results back through standard input/output. This is ideal for developers working in a local IDE or CI pipeline where network exposure is limited. In sse mode, the service launches a lightweight HTTP endpoint that pushes updates via Server‑Sent Events. This mode is perfect for distributed systems or cloud deployments where multiple assistants can subscribe to a single source of truth and receive continuous updates without polling.

Key capabilities are expressed through the MCP resource schema: a single “weiboresou” resource exposes parameters such as and , allowing an assistant to request the most recent hot topics with fine‑grained control over latency. The server also implements sampling and prompt templates, enabling assistants to format the retrieved data into concise summaries or sentiment analyses on demand. Because MCP standardizes these interactions, developers can integrate the Weibo feed into any Claude‑style workflow with minimal friction—just add the resource to their configuration and reference it in prompts.

Real‑world use cases include market research, brand monitoring, or content strategy. A marketing team can ask an AI assistant to “list the top 10 Weibo trends for the past hour” and receive a ready‑to‑share report. A news aggregator can automatically flag emerging stories for human editors, while a sentiment analysis pipeline can feed the raw posts into downstream models for real‑time public opinion tracking. The dual transport options mean that teams can run the service locally during development and then switch to a scalable SSE deployment in production without changing their AI code.

What sets Weiboresou apart is its focus on simplicity and compliance with MCP best practices. The server requires only a single configuration entry, supports both local and remote invocation, and leverages existing Python tooling (Conda environments, ) to keep the runtime footprint small. For developers who already use MCP for other tools, adding Weibo trending data becomes a plug‑and‑play addition that enriches assistant capabilities with fresh, culturally relevant information.