Run a full deliverability audit across authentication (SPF/DKIM/DMARC), domain reputation, list hygiene, content signals, and blocklist status. Returns prioritized findings.
| Name | Required | Description | Default |
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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 declare the tool as readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds only 'Returns prioritized findings,' which modestly informs outcome but does not disclose any additional behavioral traits such as execution time, rate limits, or prerequisite conditions. Given the rich annotations, the description provides acceptable but minimal added transparency.
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 extremely concise: two sentences totaling 23 words, with no redundancy. The first sentence immediately conveys the broad scope, and the second sentence succinctly describes the output format. Every word is purposeful.
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?
For a tool with no input parameters, the description covers the areas inspected (authentication, reputation, hygiene, content, blocklist) and states it returns prioritized findings. It is nearly complete but lacks mention of any prerequisites (e.g., domain verification) or error conditions, leaving minor gaps. Still highly functional for an agent.
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?
There are zero parameters, and the schema coverage is 100%. According to the guidelines, with 0 parameters the baseline is 4. The description does not need to add parameter meaning since none exist, and it correctly omits any parameter-related text.
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 a specific verb ("run") and resource ("deliverability audit"), and enumerates the components (SPF/DKIM/DMARC authentication, domain reputation, list hygiene, content signals, blocklist) that distinguish it from sibling tools like check_dmarc, validate_email, or get_deliverability_score, which are narrower in scope.
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 does not provide any guidance on when to use this comprehensive audit versus the many sibling tools. For example, it does not say 'use this for a full assessment' or contrast it with simpler checks like get_deliverability_score. The context signals include sibling names, but the description itself offers no comparative guidance.
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.