bgpt-paper-search
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server.
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Installation Guide
How to use bgpt-paper-search on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
bgpt-paper-search
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches bgpt-paper-search from connerlambden/bgpt-mcp and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate bgpt-paper-search. Access via /bgpt-paper-search in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
| 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. |
| allowed-tools | Bash |
| license | MIT |
| metadata | 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.
Claude Desktop / Claude Code
Add to your MCP configuration:
{
"mcpServers": {
"bgpt": {
"command": "npx",
"args": ["mcp-remote", "https://bgpt.pro/mcp/sse"]
}
}
}
npm (alternative)
npx bgpt-mcp
Usage
Once configured, use the search_papers tool provided by the BGPT MCP server:
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
List & Monetize Your Skill
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- GGanesh Mohane★★★★★Dec 12, 2024
bgpt-paper-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- SShikha Mishra★★★★★Dec 8, 2024
bgpt-paper-search fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- AAanya Mehta★★★★★Dec 8, 2024
bgpt-paper-search has been reliable in day-to-day use. Documentation quality is above average for community skills.
- YYash Thakker★★★★★Nov 27, 2024
bgpt-paper-search is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- EEmma Rahman★★★★★Nov 27, 2024
Useful defaults in bgpt-paper-search — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- HHassan Abbas★★★★★Nov 23, 2024
Registry listing for bgpt-paper-search matched our evaluation — installs cleanly and behaves as described in the markdown.
- DDhruvi Jain★★★★★Oct 18, 2024
Keeps context tight: bgpt-paper-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
- AAarav Gonzalez★★★★★Oct 18, 2024
I recommend bgpt-paper-search for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- HHassan Rao★★★★★Oct 14, 2024
bgpt-paper-search reduced setup friction for our internal harness; good balance of opinion and flexibility.
- LLuis Jain★★★★★Sep 25, 2024
Keeps context tight: bgpt-paper-search is the kind of skill you can hand to a new teammate without a long onboarding doc.
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