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search_rhea_entity

Search the Rhea database for biochemical reactions by keyword query, returning reaction IDs and equations.

Instructions

Search Rhea database for biochemical reactions using keyword search.

Args: query (str): Search query string. Examples: - "ATP" - find reactions involving ATP - "glucose" - find reactions with glucose - "uniprot:*" - reactions with UniProt annotations - "" - retrieve all reactions 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). limit (int, optional): Maximum number of results. Defaults to 100. Must be >= 0; a negative limit is rejected (it would make Rhea return the entire database).

Returns: JSON string: a bare array of reactions, each with 'rhea_id' and 'equation'. Empty and non-empty results share the same shape. Example: '[{"rhea_id": "RHEA:10000", "equation": "ATP + H2O = ..."}]'

Raises: ValueError: If limit is negative.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
termNo
limitNo
queryNo
searchNo
keywordNo
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 provided, the description fully explains behavior: return format (JSON string array with rhea_id and equation), Raises ValueError for negative limit, and behavior for conflicting alias values. It also notes that empty and non-empty results share the same shape.

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 well-structured with Args, Returns, and Raises sections. It is front-loaded with the core purpose and examples. Every sentence adds value; nothing is redundant.

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 the tool's complexity (8 parameters, many aliases) and no annotations, the description covers all essential aspects: search behavior, parameter semantics, output format, and error conditions. It is complete for an agent to correctly invoke the tool.

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

Parameters4/5

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

The input schema has 8 parameters with 0% description coverage, but the description compensates by explaining the main query parameter and its aliases, along with the limit parameter. It adds meaning beyond the schema by detailing alias usage and error conditions.

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 the Rhea database for biochemical reactions using keyword search. It distinguishes itself from sibling tools like search_reactome_entity and search_uniprot_entity by specifying the database explicitly.

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 detailed usage examples, explains aliases and constraints (e.g., ValueError if conflicting aliases, limit must be >=0). However, it does not explicitly compare to sibling tools or state when to use this tool over others, though the specificity to Rhea makes it implicit.

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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