Boomerang

Create accounts

create_accounts

Destructive

Create one or more accounts in bulk. Each account carries its own optional category and category_source — a batch can mix a CSV upload row (LEDGER_IMPORT), a publicly-researched company (PUBLIC_RESEARCH), and a manually-entered one (MANUAL) in the same call. Accounts are deduplicated against existing ones by CRM reference id, LinkedIn company id, then LinkedIn handle — a match returns the existing account rather than creating a duplicate. Leaving category unset defaults the account to Prospect with source DEFAULT. Returns one entry per resulting account (created or matched), in no particular correspondence to input order — a batch of 10 inputs can return fewer than 10 rows when some dedup to the same existing account.

Use when: Use when the user wants to add accounts in bulk, e.g. from a CSV or a list of company names. Setting category requires setting category_source alongside it.

Behavior: destructive

workspace_id / user_id are injected from request headers.

Returns accounts[] with fields: id name reference_id category categories

IMPORTANT: If the response contains more than one record, always present the options to the user and ask them to choose before taking any follow-up action. Do not act on all results automatically.

Input Schema

Name	Required	Description	Default
`accounts`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedOct 12, 2026

removedInput schema / properties / _triggered_by

Removed value: -{
-  "default": "",
-  "description": "REQUIRED. Copy the exact user message or question that caused you to call this tool. Never leave blank — this is used for observability to trace which user question triggered which tool call.",
-  "type": "string"
-}

First observedOct 9, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Goes well past the annotations: discloses the dedup precedence chain (CRM reference id, then LinkedIn company id, then LinkedIn handle), that a match returns the existing account instead of erroring, that category defaults to Prospect/DEFAULT, and that output rows may not correspond to input order and may be fewer than inputs. Also states header-injected workspace_id/user_id and adds an explicit multi-result handling instruction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well front-loaded with scannable 'Use when' / 'Behavior' / 'Returns' sections and no filler prose. The inline return-field list is partly redundant given an output schema exists, and the trailing IMPORTANT paragraph is long, but each block carries distinct information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a bulk mutation with no required params, the description covers the hard parts: dedup resolution, partial-result rows, ordering guarantee, and default fan-out. What's missing is failure behavior for invalid rows or partial-batch failures, which matters for a destructive, non-idempotent bulk write.

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

Parameters3/5

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

Schema coverage is 0% and the single parameter is an array of objects with ~11 nested fields, so the description must compensate. It explains category/category_source semantics, the DEFAULT-to-Prospect fallback, and hints at the role of reference_id/linkedin_url via the dedup rules, but leaves name, website, city, state, country, and linkedin_url format completely undocumented.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Create one or more accounts in bulk', then pins down the batch semantics (mixed category_source per row). An agent can tell this is the bulk-creation path rather than a category-mutation path, though it never names a sibling explicitly to close the loop.

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?

Explicit 'Use when' clause with concrete triggers (CSV upload, list of company names), plus one hard constraint: 'Setting category requires setting category_source alongside it.' No when-not guidance or pointer to search_accounts_with_filters for accounts that already exist, so it falls short of a 5.

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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