Twikit MCP Server

MCP‑Twikit is a Model Context Protocol server that bridges AI assistants with Twitter’s search and timeline APIs. By authenticating via a user’s credentials, the server exposes a set of lightweight tools that let an assistant query tweets, fetch home timelines, and perform sentiment or keyword analysis—all without the client needing direct access to Twitter’s REST endpoints. This solves a common pain point for developers: keeping sensitive API keys out of the client while still enabling rich, real‑time social media interactions.

The server’s core value lies in its contextual search capability. An assistant can ask, “What are people saying about a brand?” and the server will execute a Twitter search (e.g., ), return the most recent results, and even perform a sentiment breakdown. This is particularly useful for market researchers, PR teams, or customer support bots that need up‑to‑date public opinion. Because the search is scoped to a single account or keyword, developers can avoid unnecessary data noise and focus on actionable insights.

Key features include:

• Authenticated search: Use a logged‑in user’s credentials to perform searches on their behalf, respecting rate limits and privacy.
• Home timeline retrieval: Fetch the latest tweets from a user’s personal feed, enabling “what’s happening” queries.
• Tool‑chain integration: The server presents each operation as a callable tool, allowing an LLM to orchestrate multiple searches or combine results with other data sources.
• Sentiment summarization: After retrieving tweets, the assistant can generate a concise sentiment report, highlighting positive or negative trends and key complaints.

Typical use cases span several domains. In social media monitoring, a marketing team can compare brand sentiment across competitors with a single prompt. A customer support bot might pull the latest tweets mentioning a product to surface common issues before reaching out. In news aggregation, journalists can quickly gauge public reaction to breaking stories by querying relevant hashtags.

Integrating MCP‑Twikit into an AI workflow is straightforward: the server registers its tools with the client’s MCP runtime, and any LLM capable of calling tools can invoke them. Because authentication is handled server‑side, developers avoid exposing passwords or OAuth tokens in the client code. The server also respects Twitter’s usage policies, ensuring compliant access patterns.

What sets MCP‑Twikit apart is its focus on conversational context. Rather than exposing raw tweet data, the server offers higher‑level abstractions—sentiment summaries, filtered searches—which fit naturally into an assistant’s dialogue. This reduces cognitive load for end users and lets developers build richer, more intuitive AI experiences that leverage Twitter without the overhead of managing API keys or rate limits.