List all verified senders in your MailerCloud account. Use the returned sender IDs when creating campaigns with the sender_id parameter.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number | |
| limit | No | Number of senders to return | |
| api_key | No | MailerCloud API key |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and side-effect expectations. The description adds context that the senders are verified and scoped to the account, and that the response includes sender IDs, but it does not disclose additional behavior such as response shape, pagination semantics, or error handling. This is comparable to the calibration example where annotations lowered the bar and a 3 was appropriate.
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?
The description is two sentences with no fluff. The first sentence immediately conveys the core purpose, and the second adds actionable guidance on how to use the result. Every word earns its place, making it highly concise and front-loaded.
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?
With no output schema, the description partially covers return value semantics by stating that sender IDs are returned. Inputs are fully documented in the schema, annotations cover safety, and the description provides a use case. It lacks explicit mention of the response format, but for a simple listing tool this is not a significant gap, so it is nearly complete.
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?
The input schema covers all three parameters (page, limit, api_key) with descriptions, and schema description coverage is 100%, so the description carries no extra burden. The description's mention of sender_id is not about this tool's parameters but about a downstream consumer, so it does not enhance parameter understanding. Baseline 3 applies.
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 specifically states the tool 'List all verified senders in your MailerCloud account', which clearly conveys both the action (listing) and the resource (verified senders). It also explains the practical purpose of the returned sender IDs, making the tool's role unambiguous even among many siblings.
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?
The description gives clear context: use this tool when you need sender IDs for creating campaigns, as it says 'Use the returned sender IDs when creating campaigns with the sender_id parameter.' It does not explicitly compare to alternatives, but there is no competing sibling tool for sending senders, so this context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
