bgpt-paper-search

--- name: bgpt-paper-search description: Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone. license: MIT compatibility: Requires the BGPT MCP server configured in the agent host (npx mcp-remote or npx bgpt-mcp), internet access to bgpt.pro, and an optional BGPT API key for paid usage. metadata: {"version": "1.1", "skill-author": "BGPT", "website": "https://bgpt.pro/mcp", "github": "https://github.com/connerlambden/bgpt-mcp"} --- # BGPT Paper Search ## Overview BGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text studies. Unlike traditional literature databases that return titles and abstracts, BGPT returns structured data from the actual paper content — methods, quantitative results, sample sizes, quality assessments, and 25+ metadata fields per paper. ## When to Use This Skill Use this skill when: - Searching for scientific papers with specific experimental details - Conducting systematic or scoping literature reviews - Finding quantitative results, sample sizes, or effect sizes across studies - Comparing methodologies used in different studies - Looking for papers with quality scores or evidence grading - Needing structured data from full-text papers (not just abstracts) - Building evidence tables for meta-analyses or clinical guidelines ## Setup BGPT is a remote MCP server — no local installation required. Configure it in your agent's MCP settings before use; this skill instructs the agent to call the `search_papers` MCP tool and does not enable MCP access by itself. ### Claude Desktop / Claude Code Add to your MCP configuration: ```json { "mcpServers": { "bgpt": { "command": "npx", "args": ["mcp-remote", "https://bgpt.pro/mcp/sse"] } } } ``` ### npm (alternative) ```bash npx bgpt-mcp ``` ## Usage Once the BGPT MCP server is configured, call its `search_papers` tool via the agent's MCP interface (not via Bash): ``` Search for papers about: "CRISPR gene editing efficiency in human cells" ``` The server returns structured results including: - **Title, authors, journal, year, DOI** - **Methods**: Experimental techniques, models, protocols - **Results**: Key findings with quantitative data - **Sample sizes**: Number of subjects/samples - **Quality scores**: Study quality assessments - **Conclusions**: Author conclusions and implications ## Pricing - **Free tier**: 50 searches per network, no API key required - **Paid**: $0.01 per result with an API key from [bgpt.pro/mcp](https://bgpt.pro/mcp)

[{"step":"Install and configure the BGPT MCP server. Ensure you have access to the required API endpoints and authentication tokens.","action":"Follow the BGPT MCP server documentation to set up the connection. Test the connection with a simple query like 'list available fields' to verify functionality.","tip":"Check if your institution or organization has a pre-configured BGPT MCP server endpoint. Many universities and research institutions provide access to this service."},{"step":"Define your search parameters. Use the prompt template to specify your topic, focus area, and desired output format.","action":"Replace [TOPIC] with your research area (e.g., 'mRNA vaccine stability'). Replace [SPECIFIC_ASPECT] with a detailed criterion (e.g., 'thermostability at 37°C'). Replace [USE_CASE] with your intended application (e.g., 'meta-analysis preparation').","tip":"Be as specific as possible with your criteria. For example, instead of 'CRISPR', use 'CRISPR-Cas9 editing efficiency in human embryonic stem cells'. This will yield more relevant and structured data."},{"step":"Execute the search and review the structured output. The BGPT MCP server will return data in a standardized format with 25+ fields per paper.","action":"Use the output to identify patterns, quality scores, and experimental details. Highlight papers with quality scores above 8.0 for further review.","tip":"Sort the results by quality score to prioritize the most reliable data. Use the 'conclusion' field to quickly assess the relevance of each paper to your research question."},{"step":"Refine your search based on initial results. Adjust your parameters to focus on gaps or areas of interest identified in the first batch of papers.","action":"Modify your prompt to include additional criteria or exclude irrelevant studies. For example, add 'exclude papers published before 2020' or 'focus on in vivo studies'.","tip":"Use the 'methods' and 'results' fields to identify common experimental approaches. This can help you standardize data extraction for your literature review or meta-analysis."},{"step":"Export and format the data for your specific use case. The structured output can be directly imported into analysis tools like R, Python, or Excel.","action":"Convert the output to CSV or JSON format for easy integration with your analysis pipeline. Use tools like Pandas (Python) or Excel's Power Query to clean and transform the data.","tip":"Create a template for your data extraction to ensure consistency across multiple searches. This will save time and reduce errors when compiling evidence for your research."}]