Projects, Collaboration Datasets, and Data Schemas

This article describes what you can do with the Rhino MCP connection in the Projects and Datasets capability areas, with example prompts for each action. For Authentication, see Prerequisites and Authentication.

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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 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."

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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?"

• "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."

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."

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Collaboration

Collaboration tools let you manage who has access to a project and check the health of your federated network before running jobs.

Viewing and managing collaborators

List all collaborators on a project, invite new users or sites, or remove access for someone who should no longer be on the project.

• "Who has access to the oncology project?"

Checking workgroup and site connectivity

Before submitting a long-running federated job, check that all required sites are online and reachable.

• "Are all sites in project Xmy workgroup online?"

• "Check the connectivity status of the sites in the oncology project before I run the analysis."