Examples¶
Choose an example based on what you want URSA to do. Each published example is
a self-contained folder with setup instructions, dependencies, inputs, and
expected results. Start with an example tagged beginner if this is your first
time using an execution agent, the dashboard, or MCP.
-
Launch reusable team and symposium environments from YAML definitions.
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Attach MCP tools to a Python agent
Start a local MCP server, discover its tools, and attach them to an URSA ChatAgent.
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Use an execution agent to implement, checkpoint, and continue a Bayesian optimization workflow.
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Rank and select a constrained experiment campaign using transparent toy risk scores.
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Use URSA to query MIST, a molecular foundation model for property prediction, through Nomad's MCP server.
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Query arXiv, OSTI, and the web with URSA acquisition agents.
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Run a local SQLite MCP server, verify its tools, and connect it to the URSA dashboard.
Adding an example¶
Use one example per folder with this minimum structure:
examples/category/my_example/
├── README.md # purpose, setup, run, expected results, and cleanup
├── example.yaml # documentation metadata
├── pyproject.toml # isolated Python dependencies, even when empty
└── ... # scripts, inputs, images, and other example files
The metadata requires a title, summary, and tags. Examples are ordered alphabetically by title:
title: My example
summary: One sentence explaining what the reader will learn.
tags:
- execution-agent
- beginner
The documentation build discovers every example.yaml. It publishes the
folder's README.md at a matching /examples/.../ URL and adds it to the
catalog. Keep relative README links local to the example folder; the published
page rewrites links to adjacent files so they open the version-matched source
on GitHub.
Common tags¶
Use lowercase, hyphenated tags. Prefer these common tags so related examples remain easy to find:
| Tag | Use for |
|---|---|
guided |
A narrative walkthrough with ordered setup, execution, and review steps |
source-only |
A focused source example intended primarily to be configured and run |
beginner |
A good first example with minimal prerequisites |
tui |
Workflows driven through URSA's terminal user interface |
dashboard |
Workflows using the browser dashboard |
python-api |
Direct use of URSA classes from Python |
execution-agent |
Tasks that run commands or create workspace artifacts |
mcp |
MCP servers, clients, or tools attached to URSA agents |
multi-agent |
Teams, symposia, or other composed-agent workflows |
simulation |
Running or analyzing a scientific simulation |
plotting |
Producing plots or other visual artifacts |
optimization |
Search, experiment selection, or mathematical optimization |