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Discover scientific datasets for remote agents through an operator-owned MCP tool.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "CLIO Scientific Catalog" MCP server from askskill: Run: claude mcp add 'io-github-iowarp-scientific-catalog-mcp' -- npx -y clio-kit
Use CLIO Scientific Catalog to find public scientific datasets related to single-cell RNA sequencing, and list the results by dataset name, topic, and availability.
A list of discovered relevant scientific datasets for further filtering and research.
Using CLIO Scientific Catalog, find climate change observation datasets for a remote agent, prioritizing resources related to temperature, rainfall, and long-term trends.
A candidate dataset list suitable for agent-driven work, focused on the requested scientific topic.
Researchers can use it to discover relevant scientific datasets before finalizing a topic or method, helping them assess whether usable data exists. It fits workflows where remote agents assist with search.
Data analysts can use the tool to find dataset sources relevant to a scientific question before starting analysis. This speeds up early-stage data discovery and selection.
It is an MCP tool for discovering scientific datasets for remote agents. Based on the name and description, it emphasizes scientific dataset discovery and an operator-owned setup.
It is suitable for researchers, data analysts, and developers who want AI agents to perform scientific dataset discovery. It is more focused on research data discovery than general web search.
The provided materials do not include installation steps, runtime details, or key requirements. See the source repository for integration details.
Access, inspect, and work with Apache Parquet data files efficiently.
Analyze saved earthquake catalogs for sequence characteristics and completeness magnitude metrics.
Manage, automate, and monitor high-performance computing pipelines and research workflows.
Perform advanced data analysis and dataframe operations with comprehensive Pandas capabilities.
Process and sort large log files in parallel for faster analysis.
Connect to Slurm clusters to inspect jobs, queues, and HPC scheduling tasks.
Access research workflows, retrieval tools, and knowledge resources for faster analysis.
Search, filter, and analyze academic literature across multiple research databases.
Search papers, parse full-text PDFs, extract details, and manage citations.
Search and cross-reference prior art across papers, patents, books, and standards.
Explore research metadata and citation links across papers, datasets, and software.
Search the web locally and generate grounded answers with an Ollama model.