The mcp-tavily solution is an MCP server that empowers AI models with advanced search and content extraction capabilities via the Tavily API. It offers a suite of tools, including basic search, context-aware search, question-and-answer focused search, and URL content extraction. These tools enable AI models to access and process real-time information, enhancing their ability to provide accurate, contextually relevant responses.

mcp-tavily seamlessly integrates with AI models through the MCP framework, allowing developers to easily incorporate its functionality into their applications. By providing configurable options for search depth, filtering, and content inclusion, this solution addresses the developer pain point of efficiently gathering and structuring external data for AI model consumption. The core value lies in its ability to augment AI models with up-to-date information, improving their performance and utility across various applications. It can be installed via npm or used directly with npx, and configured with your Tavily API key.

Diverse Search Functionality

The mcp-tavily server offers three distinct search tools: basic search (search), context-aware search (searchContext), and question-answering focused search (searchQNA). The basic search allows for general queries with customizable options like search depth, topic, and the maximum number of results. Context-aware search enhances relevance by considering the context of the query, enabling more precise results. The question-answering search focuses on providing direct answers to questions, making it ideal for information retrieval tasks. These tools empower AI models to access and process information from the web in a targeted and efficient manner.

For example, an AI assistant could use searchQNA to answer user questions, searchContext to gather information related to a specific topic, and search to perform broad research. The availability of these different search methods allows developers to tailor the search process to the specific needs of their AI model.

Configurable Content Extraction

The extract tool allows AI models to extract content from specified URLs with configurable options. This feature enables the AI to retrieve and process information directly from web pages, which is crucial for tasks like content summarization, sentiment analysis, and data mining. The extraction depth can be adjusted to control the level of detail retrieved, and options are available to include images in the extracted content. This functionality allows AI models to access and utilize web-based information effectively.

For instance, an AI model designed to summarize news articles could use the extract tool to retrieve the content of multiple articles and then generate a concise summary. Similarly, a sentiment analysis model could use this tool to gather customer reviews from various websites and analyze the overall sentiment. The ability to configure the extraction process ensures that the AI model retrieves the most relevant information for its specific task.

Rich Search Configuration Options

mcp-tavily provides extensive options for customizing search queries, including search depth, topic filtering, time range, and domain inclusion/exclusion. The searchDepth option allows users to specify the level of detail in the search results, while the topic option enables filtering results based on predefined categories like news or finance. The timeRange option allows users to restrict search results to a specific period, such as the past week or month. Furthermore, users can specify domains to include or exclude from the search results, providing fine-grained control over the information sources used.

For example, a financial analysis AI could use the topic filter to focus on financial news and the timeRange filter to analyze recent market trends. A brand monitoring AI could use the includeDomains and excludeDomains options to focus on specific review sites and exclude irrelevant forums. These configuration options enable developers to tailor the search process to the specific needs of their AI model, ensuring that it retrieves the most relevant and accurate information.