This article describes what you can do in the Projects and Datasets capability areas, with example prompts for each action. For Authentication, see Prerequisites and Authentication.
Note: The underlying tool names and implementation details in this section are subject to change as the MCP server evolves. You do not need to know the tool names to use the MCP server — simply describe what you want to do in natural language and your AI assistant will handle the rest.
Projects
Projects are the top-level organisational unit in Rhino FCP. Each project contains datasets, code objects, runs, and a set of collaborating sites. You can list, inspect, create, and delete projects through natural language.
Listing and viewing projects
Ask your AI assistant to show you the projects you have access to, or to get details on a specific project.
- "List my Rhino Health projects."
- "What projects do I have access to?"
- "Show me the details for the Multi-site Oncology Study project."
- "How many sites are in my cardiology project?"
Creating a project
Ask your assistant to create a new project in your workgroup. You can provide a name and an optional description.
- "Create a new project called 'Diabetes Cohort Study'."
- "Set up a new Rhino project for our Q3 multi-site trial. Give it a description explaining it covers sites in the EU and US."
Deleting a project
Project deletion is permanent — all job history, collaborator settings, and configuration will be lost. Datasets stored at sites are not affected.
- "Delete the 'Pilot Study 2023' project."
Datasets
Datasets represent data files registered within a project. They can be local (single-site) or federated (spanning multiple sites). The MCP server lets you list, register, sync, profile, compare, and remove datasets through conversation.
Listing and exploring datasets
Ask your assistant to show datasets available in a project, or to retrieve the schema of a specific dataset.
- "What datasets are available in the oncology project?"
- "Show me only the datasets I uploaded."
- "What are the column names and types in the 'patient_cohort_2024' dataset?"
- "Show me the schema for the admissions dataset."
Registering a dataset
You can register a file that already exists at a site as a dataset in the platform, or create a federated dataset that spans multiple sites.
- "Register the file at '/data/cohort_jan2025.csv' as a new dataset called 'Cohort Jan 2025' in the oncology project."
- "Create a federated dataset called 'Multi-site Admissions' using the admissions files across all three sites."
Syncing a dataset
After files are updated at a site, sync the dataset to refresh its metadata, row counts, and schema in the platform.
- "Sync the 'patient_cohort_2024' dataset."
- "Refresh the metadata for the admissions dataset."
Profiling a dataset
Generate a statistical profile to understand your data before running analytics. Profiling returns null rates, value distributions, and summary statistics without exposing any raw rows.
- "Profile the 'patient_cohort_2024' dataset."
- "Give me a statistical summary of the age, gender, and diagnosis columns in the admissions dataset."
- "What percentage of values are missing in each column of the trial dataset?"
Comparing datasets
Compare datasets — or the same dataset across sites — to check for distribution drift, schema differences, or cohort divergence before running federated analytics.
- "Compare the baseline and follow-up datasets for distribution differences."
- "Check whether the admissions data is consistent across all three sites."
- "Are there any schema mismatches between the two cohort datasets?"
Deleting a dataset
Deleting a dataset removes the platform record only — the underlying file at the site is not affected.
- "Delete the 'old_cohort_2022' dataset from the project."
- "Remove the federated admissions dataset."