Visualize the engagement funnel for a sent campaign: total sent → delivered → opened → clicked. Shows conversion rates at each stage, identifies the biggest drop-off point, and provides targeted recommendations to fix the weakest stage of the funnel.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | MailerCloud API key | |
| campaign_id | Yes | Campaign ID for funnel analysis,required |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds value by explaining the analytical behavior (showing conversion rates, identifying the largest drop-off, providing recommendations) without contradicting annotations. It does not disclose any side effects beyond the safe read-only nature, but adds functional context for the agent.
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 definition is two sentences with no redundant fluff. It front-loads the core action (Visualize), then specifies the resource (engagement funnel for a sent campaign), and clarifies the analytical output. Every clause contributes to understanding the tool's function without unnecessary detail.
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?
At low complexity (2 parameters, 1 required, schema covers all), and with full annotations covering safety, the description adequately covers what the tool does with outputs like conversion rates and recommendations. However, there is no output schema, and the description does not explicitly state the return format or how the recommendations will be presented, leaving minor ambiguity about the exact shape of the answer.
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 provides descriptions for both parameters (api_key and campaign_id) with 100% coverage, so the description adds little beyond the schema. The description does imply the campaign_id is the central target of analysis ('for a sent campaign') which slightly enhances context, but no additional semantic details beyond schema are provided, keeping the score at the baseline for full schema coverage.
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 clearly states the tool's purpose, with a specific verb ('Visualize'), resource ('engagement funnel for a sent campaign'), and enumerates the funnel stages (total sent → delivered → opened → clicked). It also mentions specific analytical outputs (conversion rates, biggest drop-off point, recommendations), which distinguishes it from more general analysis tools like analyze_campaign or compare_campaigns by implying a funnel-specific focus.
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 specifies a concrete use case ('for a sent campaign') and implies usage when you need funnel analysis. However, it does not explicitly contrast this tool with alternatives (e.g., when to use analyze_campaign instead), nor does it mention exclusion scenarios, but the context is clear enough that an agent can infer appropriate usage.
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.
