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beel_create_customers_bulk

Create up to 500 customers in a single atomic call. Use dry_run to validate NIFs, duplicates, and formats before persisting.

Instructions

Creates up to 500 customers of this company (NIF) in a single call. The operation is atomic: if any customer fails validation the whole batch is rejected with 422 BULK_VALIDATION_ERROR and nothing is persisted.

With dry_run=true the batch is only validated — tax identifiers against the AEAT register, duplicates inside the batch and against the existing customers, field formats — nothing is written and the answer is 200. With dry_run=false, the default, validation is followed by creation and the answer is 201. Both modes return the same per-record report, so a dry run and a real run are read the same way.

Endpoint: POST /v1/companies/{company_id}/customers/bulk

⚠️ Fiscal guardrails — read before calling:

  • Why a name that does not match the census makes an invoice unsubmittable. (resource: beel://guardrails/nif-validation)

For the exhaustive rules and worked examples, call beel_docs_search.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes
dry_runNoValidate the batch without persisting it (`true`), or validate and create it (`false`, the default). Either way the batch is atomic.
company_idYesNIF (company) the operation acts on. It is the only source of context: the account that owns it is derived from it, and the `BeeL-Active-Company` header plays no part. A NIF you do not reach answers `403`, and so does a NIF that does not exist, so the existence of a NIF in another account is never disclosed.
idempotency_keyNoOptional idempotency key for this operation. Omit it and one is derived from the request itself, which makes a blind retry safe but also collapses a SECOND, deliberately identical operation into the first for 24 hours. Set it — to an order id, or anything unique per intended operation — whenever you mean to create something that may look identical to what you just created.
Behavior5/5

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

Beyond the annotations (which already show readOnlyHint=false), the description discloses atomicity, the exact HTTP status codes per mode (422 for validation failure, 200 for dry run, 201 for real run), the validation scope (AEAT register, duplicates, field formats), and that both modes return the same per-record report. It also clarifies the `company_id` as the sole source of context and that a non-existent NIF returns 403 — all useful behavioral context the annotations do not cover.

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?

The description is structured with clear headers, bullet points, and a warning callout. It front-loads the core purpose and the atomic semantics, then expands into modes and guardrails. While longer than the ideal, every sentence earns its place; there is no redundancy or fluff.

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?

Given the rich input schema (which includes all field details) and the absence of an output schema, the description covers the operation's behavior, modes, validation rules, and points to external documentation for exhaustive details. It mentions the per-record report but does not describe its structure; that minor gap is acceptable as the schema already covers inputs and the report format is not critical for invocation correctness.

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 description coverage is 75% and the schema itself has very detailed parameter descriptions (e.g., `company_id`, `dry_run`, `idempotency_key`, and the nested customer schema). The description adds the atomicity and validation behavior but does not clarify parameter meaning beyond what the schema already provides. It does not need to, but it also does not add significant value on top; hence baseline 3.

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 states a specific verb ('Creates'), a precise resource ('customers of this company (NIF)'), and adds a clear bound ('up to 500'). It unambiguously distinguishes this bulk tool from the single-create sibling `beel_create_customer` and from other bulk tools like `beel_create_products_bulk`.

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?

The description explains the two operational modes (`dry_run` vs. `dry_run=false`) and when each is appropriate, and explicitly directs the agent to `beel_docs_search` for exhaustive rules. It does not explicitly contrast with sibling tools, but the 'bulk' and 'up to 500' phrasing makes the use case obvious. Missing an explicit 'use this instead of X' but strong enough for a 4.

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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