Well

Get own company

well_get_own_company

Read-only

Get which company the workspace itself is: the confirmed own-company anchor (anchor) and any detected companies not yet confirmed as it (candidates).

Use this whenever a question turns on "mine" versus "theirs" — my payables, my receivables, invoices I owe, what we billed — and then filter by the company_id this returns. Never decide which records are the workspace's own by comparing a company NAME: the same legal entity appears under several labels (a registered name, a trade name, a bank-issued label), so a name filter silently drops rows.

Returns anchor (company_id, registered_name, trade_name) or null when the workspace has not resolved one yet, and candidates (each with company_id, names, role, confidence_score, state).

anchor: null means the workspace has no confirmed own company. Say so plainly and do not promote a candidate to the anchor yourself — a candidate is a detection, not a decision, and confirming one is a user action.

Registry tax ids and registered addresses are deliberately not returned.

Call this directly — no other tool call is needed first. Both the anchor and the candidates are read from the same workspace this call is scoped to.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`workspace_id`	No	Target 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

Name	Required	Description
`error`	No
`anchor`	Yes
`success`	Yes
`candidates`	Yes
`resolved_workspace`	No	The workspace that answered, when the caller named none and the token authorizes several.

TDQS

A4.7/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond the readOnlyHint: it explains the `anchor: null` case, warns against promoting a candidate to anchor, explicitly states that registry tax ids and registered addresses are not returned, and confirms no prerequisite call is needed. No contradiction with 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?

The description is thoroughly organized: definition first, usage guidance second, return shape and edge cases after. Every paragraph earns its place, and there is no fluff or repeated schema content.

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?

For a simple read-only lookup with one optional parameter, the description is complete: return shape, null behavior, deliberate omissions, scoping, and usage rules are all covered. The output schema exists, so return value details do not need to be exhaustively repeated.

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% and the single optional parameter is well documented. The description adds extra semantic value by stating that both anchor and candidates are read from the same workspace the call is scoped to and that no other tool call is needed first.

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: 'Get which company the workspace itself is' and clarifies the distinction between `anchor` and `candidates`. This makes the tool's purpose immediately distinguishable from related tools like `well_set_own_company` and `well_get_entity`.

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?

It gives an explicit when-to-use: 'Use this whenever a question turns on "mine" versus "theirs"' with concrete examples, and an explicit when-not-to: 'Never decide which records are the workspace's own by comparing a company NAME.' It does not name an alternative tool by name, but the usage context is strong and would not mislead an agent.

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.

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