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load_example_dataset

Load built-in example lineage-tracing datasets for demos and testing without your own data. Returns a TreeData with tree and expression ready for plotting and heritability analysis.

Instructions

Load a built-in pycea example lineage-tracing dataset (downloaded on first use).

Great for demos and testing without your own data. Each returns a TreeData with a tree and (for most) expression, ready for plotting and heritability.

Datasets:

  • "packer19": C. elegans embryo lineage (option {"tree": "full"|"observed"}).

  • "yang22": mouse tumor phylogenies (option {"tumors": "3435_NT_T1"}).

  • "koblan25": prime-editing lineage tracing (option {"experiment": "tumor"}).

Args: name: One of packer19, yang22, koblan25. options: Keyword args forwarded to the pycea.datasets loader.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNopacker19
optionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
n_obsYesNumber of observations (cells/leaves).
treesNoKeys in tdata.obst.
layersNo
n_varsYesNumber of variables (e.g. genes).
sourceNoOrigin path or generator.
uns_keysNo
obsm_keysNo
dataset_idYes
obs_columnsNo
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description fully compensates: notes 'downloaded on first use' and describes return type and content (TreeData with tree and expression). No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise and well-structured with introduction, use case, dataset list, and args section. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers parameter semantics, behavior, and return type adequately. Could explicitly mention when not to use vs load_dataset, but overall complete for tool usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description defines name parameter with three named options and their specific option keys, and options parameter explained as forwarded kwargs. Adds full meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states 'Load a built-in pycea example lineage-tracing dataset' with specific verb and resource. Lists datasets with distinct examples, differentiating from siblings like load_dataset.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

States 'Great for demos and testing without your own data', providing clear usage context. Does not explicitly exclude alternatives or compare to siblings, which would be ideal.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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