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robot_extract

Extract a subset module from an ontology by specifying seed terms and choosing an extraction method (STAR, BOT, TOP, MIREOT, or subset).

Instructions

Extract a subset module from a larger ontology.

Methods:

  • STAR: minimal module with seed terms and direct relationships

  • BOT: seed terms plus all superclasses

  • TOP: seed terms plus all subclasses

  • MIREOT: hierarchy-preserving extraction with upper/lower boundaries

  • subset: seed terms with materialized existential relationships

Specify seed terms via term (list of CURIEs/IRIs) or term_file.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputNo
outputNo
methodNoSTAR
termNo
term_fileNo
upper_termNo
lower_termNo
branch_from_termNo
importsNo
individualsNo
intermediatesNo
copy_ontology_annotationsNo
annotate_with_sourceNo
sourcesNo
working_directoryNo
catalogNo
prefixesNo
add_prefixNo
noprefixesNo
verboseNo
strictNo
xml_entitiesNo
extra_argsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description must carry full behavioral disclosure. It does not mention side effects, error behavior, how missing terms are handled, or whether the tool modifies the original ontology. The description only covers the extraction methods and seed term specification, lacking transparency on key behavioral traits.

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?

The description is concise and front-loaded with the main purpose in the first sentence. The list of methods is well-structured with brief explanations. Every sentence adds value without redundancy.

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

Completeness2/5

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

Given the tool has 23 parameters and no annotations, the description is incomplete. It omits explanations for many parameters and does not describe the output despite an output schema existing. The description covers only the core extraction logic and methods, leaving the agent without sufficient context for full usage.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needs to explain many parameters. It only explains 'term', 'term_file', and hints at 'upper_term'/'lower_term' for MIREOT. The remaining 21 parameters (e.g., imports, individuals, intermediates) are not described, leaving the agent with minimal semantic understanding.

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?

The description states exactly what the tool does: 'Extract a subset module from a larger ontology.' It lists specific methods (STAR, BOT, TOP, MIREOT, subset) with brief explanations, clearly distinguishing itself from sibling tools like robot_filter or robot_remove, which perform different operations.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use robot_extract versus sibling tools like robot_filter or robot_remove. It does not explain the context for using different extraction methods relative to alternatives, leaving the agent to infer usage without explicit direction.

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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