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geneontology

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by geneontology

search_bioentities

Search for bioentities (genes/proteins) across organisms using name, taxonomy, type, and source filters, with pagination.

Instructions

Search for bioentities (genes/proteins) using Gene Ontology data.

Searches across gene and protein names/labels with optional taxonomic filtering. Provides access to comprehensive bioentity information from GOlr.

Args: text: Text search across names and labels (e.g., "insulin", "kinase") taxon: Organism filter - accepts NCBI Taxon ID with or without prefix (e.g., "9606", "NCBITaxon:9606" for human) bioentity_type: Type filter (e.g., "protein", "gene") source: Source database filter (e.g., "UniProtKB", "MGI", "RGD") limit: Maximum number of results to return (default: 10) offset: Starting offset for pagination (default: 0)

Returns: Dictionary containing search results with bioentity information

Examples: # Search for human insulin proteins results = search_bioentities( text="insulin", taxon="9606", bioentity_type="protein" )

# Find mouse kinases from MGI
results = search_bioentities(
    text="kinase",
    taxon="NCBITaxon:10090",
    source="MGI",
    limit=20
)

# Search for any human genes/proteins
results = search_bioentities(
    taxon="9606",
    limit=50
)

# Find specific protein types
results = search_bioentities(
    text="receptor",
    bioentity_type="protein",
    limit=25
)

# Search across all organisms
results = search_bioentities(text="p53")

# Pagination example
page1 = search_bioentities(text="kinase", limit=10, offset=0)
page2 = search_bioentities(text="kinase", limit=10, offset=10)

# Common organisms:
# Human: "9606" or "NCBITaxon:9606"
# Mouse: "10090" or "NCBITaxon:10090"
# Rat: "10116" or "NCBITaxon:10116"
# Fly: "7227" or "NCBITaxon:7227"
# Worm: "6239" or "NCBITaxon:6239"
# Yeast: "559292" or "NCBITaxon:559292"

Notes: - Results include ID, name, type, organism, and source information - Text search covers both short names/symbols and full descriptions - Taxon IDs automatically handle NCBITaxon: prefix normalization - Use pagination for large result sets - Sources include UniProtKB, MGI, RGD, ZFIN, SGD, and others

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNo
taxonNo
bioentity_typeNo
sourceNo
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Without annotations, the description carries full burden. It discloses use of GOlr, result fields (ID, name, type, organism, source), taxon prefix normalization, and pagination. Minor omission: no mention of behavior on empty results or potential performance/rate limits.

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

Conciseness4/5

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

The description is somewhat long due to many examples and a notes section, but it is well-structured with Args, Returns, Examples, and Notes. It is front-loaded with the purpose sentence. Could trim redundant example phrases, but still concise enough.

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?

Given 6 parameters (none required) and no annotations, the description covers essential behavior: input details, examples, output structure (dictionary with bioentity info), pagination, and common organisms. However, it does not detail the output schema fields despite the tool having one, and could mention error handling.

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 the description provides detailed meaning for all 6 parameters: 'text' is described as text search across names/labels, 'taxon' as organism filter with prefix handling, 'bioentity_type' as type filter, 'source' as database filter, 'limit' and 'offset' for pagination. Examples reinforce usage.

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 clearly states it searches for bioentities (genes/proteins) using Gene Ontology data. It specifies the verb 'search' and the resource 'bioentities', distinguishing it from sibling tools like search_annotations which focus on annotations.

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?

The description provides extensive examples covering various use cases (e.g., by taxon, type, source) and explicit pagination guidance. It implicitly differentiates from siblings (e.g., search_annotations for annotation-based queries) but lacks direct 'when not to use' statements.

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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