List all reply-to email addresses configured in your MailerCloud account. Use the returned IDs when creating campaigns with the reply_id parameter.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number | |
| limit | No | Number of reply emails 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, so the tool's safeness is disclosed. The description adds the context that it returns IDs usable in campaigns, and that it lists 'all' reply emails, which is a small behavioral note. No additional risks or hidden side effects are described, but the annotations carry most of the burden.
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?
Two short sentences with no redundant phrasing. The core purpose is stated first, then the actionable usage tip. Every word contributes to clarity, and it is front-loaded with the main action.
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?
The description is complete enough for a simple list/read operation, thanks to annotations covering the safety profile and the schema covering all parameters. The main minor gap is that it does not mention pagination behavior or the exact shape of the response (though no output schema exists), but it does explicitly flag the use of IDs, which is the key return info. For a tool of this complexity, it is mostly 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 schema covers all three parameters (page, limit, api_key) with a 100% description coverage. The tool description adds no further semantics beyond what the schema provides, but it does not retreads the schema either. Per the baseline, a 3 is warranted because the schema does the heavy lifting and the description does not contradict it.
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 names a specific verb (Listing) and resource (reply-to email addresses), and immediately ties them to a concrete downstream use (campaign creation with reply_id). This clearly distinguishes it from sibling tools like list_senders or list_webhooks, which target different resources.
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?
It implies the main use case ('Use the returned IDs when creating campaigns'), which gives context but does not explicitly say when not to use it or name alternatives. Sibling tools like list_senders exist but are never mentioned, so the guidance is implicit rather than explicit.
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.
