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Get entity (with sub-resources)

well_get_entity
Read-only

Read ONE entity with its sub-resources nested in a single call.

Convenience over well_get_schema + well_query_records: resolves the field paths for you and returns the single record with its related data expanded.

depth (relation-nesting BOUNDARY, 1-3, default 1): 1 = the entity + its direct sub-resources (emails, phones, locations, …) 2 = + the sub-resources' related scalars 3 = the full level-3 graph (LARGER payload — use when you need the whole picture) Stops at depth 3. Aggregates are excluded. Each child collection is capped at 50 rows; for a full list or to page a large child collection, use well_query_records on that child root instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesThe entity's public UUID (the value of its *_id field, e.g. company_id)
rootYesEntity type, e.g. companies | people | invoices | transactions
depthNoRelation-nesting boundary 1-3 (default 1).
workspace_idNoTarget workspace. This read reports one workspace's own data. Omit it and the token's primary workspace answers, which may not be the one you mean; the result names the workspace that did.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo
errorNo
foundYes
entityYes
successYes
columnMetaNoPer-column field meaning ({context, enrichment}) for documented columns — read this to interpret the entity's values.
fields_selectedNo
resolved_workspaceNoThe workspace that answered, when the caller named none and the token authorizes several.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavior beyond that: depth semantics, hard stop at depth 3, aggregate exclusion, 50-row cap per child collection, and the warning that omitting workspace_id may hit the token's primary workspace rather than the intended one. These are exactly the behavioral details an agent needs before calling.

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 a clear opening sentence, a comparison to alternatives, and a compact bulleted explanation of depth. Every sentence carries useful information, and the most important scoping details are front-loaded. It is longer than minimal, but the added detail is directly actionable.

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 output schema exists, the description does not need to document return values. It covers all call-critical context: exactly what the tool reads, depth boundaries, exclusions, row caps, workspace targeting behavior, and when to switch to well_query_records. Nothing essential is missing for an agent to invoke it correctly.

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?

Schema coverage is 100%, so the schema already documents all four parameters. The description adds meaningful depth semantics beyond the schema, explaining what depth 1, 2, and 3 mean, the payload-size tradeoff, and the 50-row cap. The description does not add much for id/root, but the schema already covers them well, so a 4 is appropriate.

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 opens with a specific verb and resource: 'Read ONE entity with its sub-resources nested in a single call.' It clearly states what the tool does and distinguishes it from the related well_get_schema and well_query_records flow, so an agent can tell it apart from the many sibling get_* tools.

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?

The description explicitly frames this tool as a convenience over well_get_schema + well_query_records and explains that it resolves field paths and returns a single expanded record. It also gives a concrete alternative for large child collections: use well_query_records on that child root. This is strong when-to-use and when-not-to-use guidance.

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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TDQS

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: CRUD for companies, people, and invoices; financial analytics (cash, cost, runway, holdings); connector management and invocation; schema discovery; querying; reconciliation; and contact channel management. No two tools could be confused for the same action.

Naming Consistency5/5

All tools follow the `well_verb_noun` pattern with consistent verb choices (create, get, list, update, delete, add, remove, run, resolve, query, invoke). The naming is predictable and makes the tool's purpose immediately clear.

Tool Count4/5

With 26 tools, the set is slightly above the ideal 3-15 range, but every tool earns its place given the breadth of the domain (CRM, invoicing, financial analytics, reconciliation, connector management). The count is well-scoped and not excessive.

Completeness4/5

The tool surface covers core CRUD, financial KPIs, reconciliation, and connector management. Minor gaps exist (e.g., no direct tool to update contact channels or manage accounts), but the query and schema tools allow agents to work around them, and the primary workflows are fully supported.

Resources