Generate AI subject-line variants for a campaign topic, optionally tuned to a tone and audience. Good input for create_ab_test.
| Name | Required | Description | Default |
|---|---|---|---|
| tone | No | Desired tone (default professional) | |
| count | No | How many variants to generate (default 5, max 10) | |
| topic | Yes | What the email is about | |
| audience | No | Who the email is for |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint is false and destructiveHint is false, suggesting this is a non-destructive but potentially state-changing operation (generation). The description does not add behavioral details beyond what annotations imply, such as rate limits, token consumption, or whether output is persisted. Without richer annotation context, a moderate score is 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, front-loading the core action ('Generate AI subject-line variants') and immediately providing context for downstream use. Every word is functional, and there is no redundancy or filler.
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?
Given the tool has four parameters, one required, no output schema, and annotations that are present but minimal, the description adequately covers the key use case and suggests integration with 'create_ab_test'. It could be more complete by noting the default tone is 'professional' (implied by schema default), but overall it provides sufficient context for an agent to use the tool correctly.
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 all parameters. The description adds value by clarifying that 'topic' is the core input ('what the email is about') and that 'tone' and 'audience' are optional tunings, which aligns with the schema. It reinforces the purpose of 'count' as variant generation quantity but doesn't add novel semantics beyond the schema's own descriptions.
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 generates AI subject-line variants for a campaign topic, with optional tone and audience tuning. It distinguishes itself from siblings like 'create_ab_test' by positioning its output as input for that tool, though it could more explicitly differentiate from other generation or suggestion tools.
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 provides clear when-to-use context by stating the output is good input for 'create_ab_test', which suggests a specific workflow. However, it does not mention when not to use this tool or indicate alternatives for other subject line generation methods.
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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Most tools are cleanly separated by resource and action, and the descriptions do a good job of cross-referencing related tools. However, there are several close clusters—get_analytics vs generate_report, get_deliverability_score vs run_deliverability_audit, check_dmarc vs verify_domain, and list_emails vs list_inbox_conversations vs get_email—that could cause an agent to pick the wrong one. The detailed descriptions reduce but do not eliminate this ambiguity.
Tool names follow a consistent snake_case verb_noun pattern throughout: create_, get_, list_, send_, toggle_, and so on. Even multi-word actions like select_ab_test_winner and categorize_inbox_emails stay uniform. The only slight deviation is the bare verb upgrade, but it is readable and does not break the overall pattern.
54 tools is far beyond the typical well-scoped MCP surface and lands heavily in the 'too many' range. While the domain is broad, many tools could be consolidated—multiple analytics/reporting tools, several deliverability checks, and separate email/inbox listing tools create redundancy. The sheer number increases selection overhead and makes the toolset harder for an agent to navigate reliably.
The core email marketing lifecycle is represented: domains, contacts, campaigns, templates, automations, sends, and analytics all have main operations. However, there are notable gaps—no update/delete for campaigns, templates, forms, or automations; no create/update/delete for forms; no sandbox enable/disable; and no way to install marketplace items. These are workable gaps but would cause failures for agents trying to perform full lifecycle management.