🧪 Research Tools Reference

These tools are designed for scientific, medical, and data-intensive research workflows.

These tools are designed for scientific, medical, and data-intensive research workflows.

📊 Data Processing

csv_toolkit

Advanced processing of large CSV datasets without loading the entire file into memory.

  • Operations: summarize, filter, sort, aggregate.
  • Usage: Summarize the statistics for the 'total_revenue' column in sales.csv.

plot_data

Generates high-quality visualizations from numerical data.

  • Libraries: matplotlib, seaborn, plotly.
  • Output: Returns base64 encoded images or saves PNG/PDF files.
  • Usage: Plot a correlation heatmap for the variables in experiment_results.csv.

🧬 Scientific Research

Query professional databases for academic papers.

  • Sources: PubMed (Medical), arXiv (Physics/CS).
  • Parameters: Query, DateRange, MaxResults.
  • Usage: Find matching papers for 'CRISPR off-target effects' published in 2024.

biotoolkit (Advanced)

A suite of tools for DNA, RNA, and Protein sequence analysis.

  • Capabilities: Alignment, Transcription, Translation, Motif identification.
  • Usage: Analyze this FASTA sequence for potential open reading frames.

🗺️ Knowledge & Mapping

knowledge_graph

Visualizes relationships between entities or concepts extracted from research documents.

  • Usage: Map the relationships between relevant proteins found in the last 5 papers.

💡 Research Strategy

In a Research workflow, the AI:

  1. Uses literature_search to find sources.
  2. Uses csv_toolkit to process experimental data.
  3. Uses plot_data to create visualizations for the final report.
  4. Uses knowledge_graph to synthesize a conclusion.