google-scholar-mcp
MCP ServerFreeMCP server: google-scholar-mcp
Capabilities3 decomposed
scholar article retrieval via mcp
Medium confidenceThis capability allows users to retrieve scholarly articles from Google Scholar using the Model Context Protocol (MCP). It integrates with Google Scholar's API to fetch article metadata and content based on user queries, utilizing a structured request-response pattern that adheres to MCP standards. This integration enables seamless communication between the client and the Google Scholar service, ensuring efficient data retrieval and response formatting.
Utilizes a direct integration with Google Scholar's API through MCP, enabling structured and efficient queries that are compliant with the protocol's standards.
More efficient than traditional scraping methods as it directly interfaces with the Google Scholar API, reducing overhead and improving response times.
citation formatting for retrieved articles
Medium confidenceThis capability formats citations for articles retrieved from Google Scholar into various styles (APA, MLA, Chicago). It processes the metadata received from the Google Scholar API and applies formatting rules based on user preferences. The implementation uses a modular design that allows easy addition of new citation styles and ensures compliance with academic standards.
Employs a modular formatting engine that allows for easy updates and additions of citation styles, ensuring flexibility and adherence to academic standards.
More customizable than static citation tools, allowing users to define and modify citation styles as needed.
bulk article search and retrieval
Medium confidenceThis capability enables users to perform bulk searches for articles based on a list of keywords or topics. It utilizes batch processing techniques to send multiple queries to the Google Scholar API in a single request, optimizing the retrieval process. The implementation leverages asynchronous programming to handle multiple responses efficiently, ensuring quick turnaround times for large datasets.
Implements batch processing to optimize article retrieval, allowing users to efficiently gather large amounts of research data in a single operation.
Faster than individual queries due to reduced overhead and optimized API calls, making it ideal for extensive literature reviews.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Search scientific papers with raw experimental data extracted from full-text studies. Returns methods, results, quality scores, and 25+ metadata fields per paper. 50 free searches, then $0.01/result with an API key.
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Best For
- ✓researchers looking for a streamlined way to access scholarly articles
- ✓students and researchers preparing academic papers
- ✓research teams conducting literature reviews
Known Limitations
- ⚠Dependent on Google Scholar's API availability and rate limits
- ⚠Limited to articles indexed by Google Scholar
- ⚠Limited to citation styles implemented in the system
- ⚠May not cover all nuances of citation formats
- ⚠Limited by Google Scholar's API rate limits for bulk queries
- ⚠Requires careful management of query sizes to avoid throttling
Requirements
Input / Output
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Repository Details
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MCP server: google-scholar-mcp
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