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Arun-kc

schemabrain

resolve_join

Read-onlyIdempotent

Get the canonical SQL JOIN clause between two named entities. Returns a ready-to-paste clause with column mapping, eliminating manual join writing.

Instructions

Use this when you have two entity names and need the canonical SQL join between them. Returns a ready-to-paste JOIN clause with column mapping. Use suggest_joins instead when you only have physical table names. Don't use when you need to discover what entities exist — call list_entities first.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_aYesThe first entity to join, by name (e.g. `customer`). Order doesn't matter — `resolve_join` is direction-insensitive; the response preserves the direction the join was originally confirmed in.
entity_bYesThe second entity to join, by name (e.g. `order`). Both entities must exist; call `list_entities` if unsure.
nameNoWhen 2+ canonical joins exist between the entity pair (billing vs shipping address, primary vs secondary user, etc.), pass the canonical-join name to disambiguate. Leave null to receive an ambiguity-refusal response listing the available names.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
dataNo
errorNo
confidenceNo
provenanceNo
follow_up_hintsNo
degradation_reasonNo
charter_versionNo1.2
Behavior5/5

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

Annotations already declare read-only, idempotent, non-destructive. Description adds direction-insensitivity and disambiguation behavior (null returns ambiguity refusal with names), providing valuable behavioral context beyond annotations.

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?

Four concise sentences covering purpose, usage context, and disambiguation behavior with no unnecessary words.

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 annotations (read only, idempotent) and output schema existence, description covers key behavioral aspects (direction, disambiguation, return type) completely.

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%, providing baseline. Description adds meaning: order independence for entity_a/b, requirement for entities to exist, and detailed explanation of the name parameter's behavior when null.

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?

Description clearly states verb (resolve/gets join), resource (two entity names), and output (ready-to-paste JOIN clause with column mapping). Differentiates from siblings by naming alternatives (suggest_joins, list_entities).

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 when to use (have two entity names, need canonical join), when not to use (discover entities first, use suggest_joins for physical tables), and provides context like direction-insensitivity.

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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