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search_reactome_entity

Search Reactome biological pathways, reactions, and entities by keyword. Filter results by species and entity type to find relevant pathway components.

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. If both query and an alias are given with different values, this raises ValueError (pass only one). 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 type(s). Accepts a single string (e.g., "Pathway") or a list (e.g., ["Pathway", "Reaction", "Complex"]). Validated case-insensitively against the Reactome type enum; unknown values raise ValueError (the API would otherwise silently ignore them and return unfiltered results). Valid values: Complex, Protein, Reaction, Set, Pathway, Genes and Transcripts, Chemical Compound, DNA Sequence, Polymer, Drug, RNA Sequence, OtherEntity, Cell. 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: JSON string: a bare array of results, each with 'id', 'name', and 'type' fields. Empty and non-empty results share the same shape. Example: '[{"id": "R-HSA-109581", "name": "Apoptosis", "type": "Pathway"}]'

Raises: ValueError: If query is blank or types/species are invalid.

Input Schema

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

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 behavioral traits: it raises ValueError on conflicting query aliases, invalid species (rejecting numeric NCBI taxon IDs due to API silent ignoring), invalid types, and explains the per-category result capping for rows. It also warns about API behavior that could degrade results.

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, Raises sections. It is detailed but not excessively verbose; every sentence provides useful information. Minor room for tightening, but overall appropriately sized and front-loaded with the primary purpose.

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?

Given 10 parameters, no annotations, and an output schema, the description covers all parameters, explains return format, and lists exception conditions. It is complete enough for an agent to understand the tool's behavior and usage without external documentation.

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?

The schema description coverage is 0%, so the description carries full burden. It explains all key parameters thoroughly: query accepts multiple aliases with conflict handling, species must be scientific names (not NCBI taxon IDs), types are validated case-insensitively against an enum, and rows cap per category. This adds substantial meaning beyond the bare schema.

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 'Search the Reactome knowledgebase using keyword search' with example queries ('apoptosis', 'TP53', 'cell cycle'). It distinguishes from sibling tools that search other databases (e.g., search_chembl_*, search_pdb_entity) by specifying Reactome as the target.

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

Usage Guidelines3/5

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

The description does not explicitly state when to use this tool versus alternatives among siblings. While it implies Reactome searches, it lacks direct 'use this for Reactome; for other databases use corresponding tools' guidance. The detailed parameter descriptions provide context but not explicit usage boundaries.

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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