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.
git clone https://github.com/K-Dense-AI/scientific-agent-skills.git--- 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)
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/skills/bgpt-paper-searchCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Search for scientific papers on [TOPIC] in [INDUSTRY/RESEARCH FIELD] using the BGPT paper search tool. Extract structured experimental data including methods, results, sample sizes, quality scores, and conclusions. Focus on studies published between [YEAR RANGE] and prioritize peer-reviewed journals. Return the data in a tabular format with key metrics highlighted.
# Experimental Data Extraction: CRISPR Gene Editing in Agriculture (2020-2024) ## Summary Found **12 peer-reviewed papers** matching your criteria. Below are the key experimental details extracted from full-text analysis: | **Paper** | **Year** | **Sample Size** | **CRISPR Method** | **Target Gene** | **Success Rate** | **Off-Target Events** | **Quality Score** | **Key Finding** | |-----------|---------|-----------------|-------------------|-----------------|------------------|-----------------------|-------------------|-----------------| | *Nature Plants* 18:456 | 2023 | 1,200 plants | CRISPR-Cas9 | *OsALS* | 89% | 2.1% | 9.2/10 | 3.4x yield increase in drought conditions | | *PNAS* 120(12) | 2022 | 850 callus cultures | Base editing | *TaGW2* | 76% | 0.8% | 8.7/10 | Reduced cadmium uptake by 62% | | *Science Advances* 9(15) | 2021 | 3,200 seeds | Prime editing | *ZmBZR1* | 68% | 1.5% | 9.5/10 | Enhanced drought tolerance | ## Key Insights - **Success Rate Trend**: 76-89% across studies (median: 82%). - **Off-Target Risk**: Lowest in base editing (0.8% avg). - **Top Journal**: *Science Advances* (avg quality score: 9.5). ## Next Steps 1. Download full dataset [here](link-to-bgpt-export). 2. Filter by quality score >9.0 for high-confidence results. 3. Cross-reference with [COMPANY]'s internal database for application potential. **Note**: All papers include full experimental protocols in supplementary materials.
skills-collection
Take a free 3-minute scan and get personalized AI skill recommendations.
Take free scan