Create multiple contacts at once in bulk.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | MailerCloud API key | |
| list_id | Yes | List ID for all contacts,required | |
| contacts | Yes | Array of contact objects,required |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false and destructiveHint=false, so the description is not responsible for establishing that mutation occurs. However, the description adds no behavioral context beyond the bare claim of creating contacts: it does not disclose failure handling, whether partial batches roll back, outcome for duplicate emails within a batch, required authentication beyond the api_key parameter, or limits on array size — all significant for a bulk write operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler and a clear verb-resource-scope structure. It earns its place, though it borders on under-specification rather than being economically concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a batch write over a rich nested contacts array (with per-contact overrides of list_id and id) yet the description says only 'create multiple contacts.' It omits batch success/failure semantics, interaction between top-level list_id and per-contact list_id overrides, idempotence implications (annotations state idempotentHint=false, but behavior on rerun is unexplained), and any response format — all material for a non-trivial bulk tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents api_key, list_id, and contacts. With full coverage, the baseline is 3. The description adds no parameter semantics at all, but given the schema covers the load, the tool does not need to compensate for gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Create'), resource ('contacts'), and scope ('multiple... in bulk'). This distinguishes it from the sibling create_contact without naming it directly, since the batch scope is self-evident. Loses a point for not explicitly naming the sibling it is not.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus the single-contact create_contact, nor on when not to use it. It also omits practical selection context such as batch size limits, deduplication behavior, or whether upsert/update semantics apply instead. The agent must infer from the name alone.
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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Tool purposes are largely distinct, with clear separation between CRUD operations, analytics, and deliverability tools. Some overlap exists between get_campaign, analyze_campaign, and campaign_health_dashboard, but descriptions clarify scope sufficiently.
Most tools follow a consistent verb_noun pattern (list_, get_, create_, update_, delete_, send_). Minor exceptions like campaign_health_dashboard and engagement_funnel are descriptive but break the pattern.
With 47 tools, this is a very large surface area. While the variety reflects the breadth of email marketing operations, the count exceeds what is typically manageable and suggests potential redundancy or over-scoping.
The tool set covers most core workflows: contact/list management, campaign lifecycle, templates, webhooks, analytics, and transactional email. Minor gaps like no delete for templates or tags are acceptable but not fatal.
