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Arun-kc

schemabrain

find_relevant_entities

Read-onlyIdempotent

Rank business entities matching a natural-language description of a business object, keeping the agent in domain terms instead of raw table names.

Instructions

Use this when the user describes a business object (e.g. 'our customers', 'revenue data', 'product catalog'). Returns ranked entities — domain-named bindings to physical tables — so the agent stays in business terms. Use find_relevant_tables instead when no entities are curated. Common compositions: chain to describe_entity for one entity's full shape; chain to resolve_join to wire two entities together; chain to get_metric to compute a validated aggregation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language description of the business object the user is asking about (e.g. 'customers', 'revenue', 'product catalog'). Embedded with the same model used to index column descriptions, then ranked by cosine similarity against the columns of each entity's bound table. Per-entity score is the MAX across columns.
limitNoMaximum number of ranked entity hits to return. Default 10. Use a small value (3-5) for narrow exploratory queries; use 10-20 when surveying an unfamiliar semantic layer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
dataNo
errorNo
confidenceNo
provenanceNo
follow_up_hintsNo
degradation_reasonNo
charter_versionNo1.2
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds algorithmic details: ranking by cosine similarity, scoring as MAX across columns. Discloses what the tool does without contradicting annotations. Could mention any rate limits, but not essential.

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?

Three sentences, front-loaded with purpose and usage, then alternatives and compositions. No wasted words; each sentence earns its place. Highly concise yet informative.

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 output schema exists, annotations are rich, and both parameters are fully described in schema and description, the description covers purpose, usage, algorithm, and compositions. No gaps.

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 100%, but description adds significant meaning beyond parameter names/types: explains 'query' as natural-language and how searching works (embedding, cosine similarity), and 'limit' with usage guidance (3-5 for narrow, 10-20 for survey). This is exemplary.

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 is used when the user describes a business object, returns ranked entities, and distinguishes from sibling 'find_relevant_tables'. It specifies verb ('find'), resource ('relevant entities'), and scope ('business terms').

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

Usage Guidelines5/5

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

Explicitly says 'Use this when...' and 'Use find_relevant_tables instead when...' and lists common compositions (chain to describe_entity, resolve_join, get_metric). Provides clear when-to-use and alternatives.

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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