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Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server.

Search Google Patents (~140M+ worldwide publications) by free-text query, assignee, inventor, country, status, language, or date range — returns publication number, title, priority/filing/grant/publication dates, inventor, assignee, snippet, PDF URL, and canonical landing-page URL for each match.

Research topics and create compelling content for blogs, articles, and marketing materials.

Web toolkit powered by Exa for scientific and technical content search and URL extraction.

### Clinical Trials Database - name: "clinical-trials-database" - description: "Query ClinicalTrials.gov via APIv2. Use when you want to search for trials by condition, drug, location, status, or phase; retrieve trial details by NCT ID; check eligibility/inclusion criteria; count..."

Academic research documents with peer-reviewed sources, IEEE citations, and scholarly rigor. \n \n Supports multiple document types: research papers, literature reviews, theses, dissertations, conference papers, and technical reports with standard academic structure \n Integrates source discovery from Google Scholar, IEEE Xplore, PubMed, ACM Digital Library, and arXiv with built-in verification for peer-review credibility and author credentials \n Generates IEEE-format references automatically w

Orchestrates multi-source web research by delegating to subagents, synthesizing findings, and producing cited reports. \n \n Breaks research questions into 2–5 distinct subtopics, creates a research plan file, and spawns up to 3 subagents in parallel for efficient investigation \n Each subagent performs 3–5 web searches per subtopic and writes findings to local files with key facts, quotes, and source URLs \n Synthesizes results by reading local findings files, integrating insights across subtop

aris-autonomous-ml-research

### Pytorch Lightning - name: "pytorch-lightning" - description: "Deep learning framework (PyTorch Lightning / lightning package). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, T..." - allowed-tools: "Read Write Edit Bash"

### Transformers - name: "transformers" - description: "Hugging Face Transformers for loading Hub models, running pipeline inference, text generation, and Trainer fine-tuning on NLP, vision, audio, and multimodal tasks. Use when working with AutoModel, pip..."