Select the winning variant and send it to the remaining audience. This triggers a real send to everyone who was held back — it cannot be undone.
| Name | Required | Description | Default |
|---|---|---|---|
| metric | No | Metric the decision was based on (default open_rate) | |
| test_id | Yes | A/B test ID | |
| winner_variant | Yes | Label of the winning variant, e.g. B |
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 already mark destructiveHint=true. The description adds context by stating 'triggers a real send' and 'cannot be undone', reinforcing the irreversible nature. It does not contradict annotations and provides meaningful behavioral insight beyond the annotations.
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 sentences, front-loaded with the core action, no extraneous words. Every sentence earns its place by stating the action and its irreversible consequence.
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 covers the main action and consequence, but does not mention expected return values, prerequisites (e.g., test state), or error conditions. Given no output schema, slightly more detail on outcome would improve completeness.
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 (test_id, winner_variant, metric). The description adds no additional meaning or guidance for any parameter, achieving only the baseline.
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 selects a winning variant and sends it to the remaining audience, using a specific verb and resource. It distinguishes from siblings like create_ab_test and list_ab_tests by focusing on the selection and execution step.
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 implies this is the final action in an A/B test (triggering a real send) and warns it cannot be undone, but it does not explicitly state when not to use it or suggest alternative tools for previewing or cancelling.
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.
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.