mcp-zotero is an MCP server designed to seamlessly connect AI models, particularly Claude, with your Zotero library. This tool empowers AI to interact with your research data, enabling tasks like summarizing papers, answering questions based on your sources, and organizing your research. It provides a suite of tools, including get_collections, get_collection_items, get_item_details, search_library, and get_recent, allowing the AI to browse, search, and retrieve information from your Zotero collections.

By acting as a connector between Claude and Zotero Cloud, mcp-zotero eliminates the need for manual data transfer, streamlining research workflows. Developers can easily integrate this server into their Claude Desktop configuration by providing their Zotero API key and user ID. This unlocks the potential for AI-driven research assistance, making it easier to manage and utilize your Zotero library within AI applications. The server is built using TypeScript and distributed via npm, ensuring easy installation and use.

Zotero Library Access

mcp-zotero allows AI models like Claude to directly access and interact with a user's Zotero library. This is achieved by establishing a connection to the Zotero API using the user's API key and user ID. The server then exposes a set of tools that the AI model can use to query and retrieve information from the library. This includes listing collections, retrieving items within a collection, and obtaining detailed information about specific papers. This functionality is crucial for AI models that need to perform tasks such as literature reviews, research summarization, or citation analysis. For example, an AI could use get_collection_items to retrieve all papers in a "Machine Learning" collection and then use get_item_details to extract the abstract and keywords for each paper. The server uses the Zotero API to fetch data and formats it into a structure that the AI model can easily understand.

Intelligent Information Retrieval

The search_library tool enables AI models to perform intelligent searches within a user's Zotero library. Instead of relying on simple keyword matching, the AI model can leverage its natural language understanding capabilities to formulate more complex and nuanced search queries. This allows for more precise and relevant results, saving the user time and effort. For instance, a researcher could ask the AI to "find papers on the application of transformers in natural language processing published after 2020." The AI would then translate this natural language query into a series of API calls to Zotero, filter the results based on the publication date, and return a list of relevant papers. This feature significantly enhances the AI's ability to extract specific information from the Zotero library, making it a valuable tool for research and knowledge discovery. The server handles the complexity of translating natural language queries into Zotero API requests and presents the results in a structured format.

Real-time Updates

The get_recent tool provides AI models with access to the most recently added papers in a user's Zotero library. This allows the AI to stay up-to-date with the latest research and developments in a particular field. This is particularly useful for researchers who need to monitor new publications on a regular basis. For example, an AI could be configured to automatically retrieve the most recent papers added to a Zotero library and summarize them for the user. This would save the user time and effort by providing them with a concise overview of the latest research. The server periodically queries the Zotero API for new items and makes them available to the AI model through the get_recent tool. This ensures that the AI always has access to the most current information.

Integration Advantages

mcp-zotero seamlessly integrates with the MCP ecosystem, providing a standardized way for AI models to access and interact with Zotero libraries. By adhering to the MCP protocol, mcp-zotero ensures interoperability with other MCP-compatible tools and services. This allows developers to easily build AI-powered applications that leverage the wealth of information stored in Zotero libraries. The use of environment variables for Zotero API key and user ID ensures secure access to the Zotero library. The server can be easily deployed and configured using npm, making it accessible to a wide range of users. The clear documentation and troubleshooting guide further simplify the integration process.