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search_reactome_entity

Search the Reactome knowledgebase for biological pathways, reactions, and complexes using keywords, with optional filters by species and entity type.

Instructions

Search the Reactome knowledgebase using keyword search.

Args: query: The search query string (e.g., "apoptosis", "TP53", "cell cycle"). Accepts aliases: search, term, keyword, keywords, search_term, name. species: Filter by species. Must be the scientific name (e.g., "Homo sapiens", "Mus musculus"). Numeric NCBI taxon IDs like "9606" are rejected here (this tool raises ValueError) because the Reactome API silently ignores them AND can degrade co-occurring filters (e.g. types). Accepts a single string or a list of strings. types: Filter by entity types. Accepts a single string (e.g., "Pathway") or a list (e.g., ["Pathway", "Reaction", "Complex"]). rows: Per-category result cap. Reactome clusters results by entity type (cluster=true), so rows=30 returns up to 30 hits per type, not 30 hits total. To bound the total, constrain types to a single value.

Returns: List of results with 'id', 'name', and 'type' fields. Example: [ {'id': 'R-HSA-109581', 'name': 'Apoptosis', 'type': 'Pathway'}, {'id': 'R-HSA-204981', 'name': '14-3-3epsilon...', 'type': 'Reaction'} ]

Example: >>> results = search_reactome("apoptosis", rows=5) >>> for entry in results: ... print(f"{entry['type']:10} {entry['id']}: {entry['name']}")

>>> # Filter by type
>>> pathways = [r for r in results if r['type'] == 'Pathway']

Raises: httpx.HTTPError: If the API request fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
speciesNo
typesNo
rowsNo
searchNo
termNo
keywordNo
keywordsNo
search_termNo
nameNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations, the description fully discloses behavior: keyword search, species filter with critical caveat about NCBI IDs causing silent errors and degrading filters, types filter, rows per-category cap, return format, and error raising. 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.

Conciseness4/5

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

The description is well-structured with Args, Returns, Example, Raises. It is thorough but somewhat verbose, especially the list of aliases. Still, every section adds value and it is front-loaded.

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

Completeness5/5

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

Covers all aspects: parameters, special behaviors (species validation, rows per-category), return format with example, error conditions. No output schema provided, but the description sufficiently documents the return structure.

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%, so the description must add meaning. It explains each parameter: query (with aliases), species (scientific name requirement and warning), types (single or list), rows (per-category cap). The many alias parameters are correctly explained as aliases for query.

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

Purpose4/5

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

Clearly states 'Search the Reactome knowledgebase using keyword search,' specifying verb and resource. However, it does not distinguish this tool from sibling search tools (e.g., search_chembl_molecule, search_pdb_entity).

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?

No explicit guidance on when to use this tool versus alternatives. Provides usage examples but lacks comparison to other tools. The warning about NCBI taxon IDs is behavioral, not a usage guideline.

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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