Update the Gmail sending schedule: max_sends_per_day (1-100, applies account-wide across all campaigns) and/or auto_send_enabled (false stops ALL sending, true resumes it). Confirm with the user before changing these; they control real email volume.
| Name | Required | Description | Default |
|---|---|---|---|
| auto_send_enabled | No | Master switch for the hourly send sweep | |
| max_sends_per_day | No | Daily send cap across all campaigns (1-100) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description discloses critical behavioral impact: that setting auto_send_enabled=false halts all sending, and that these settings 'control real email volume'. It also explicitly instructs to confirm with the user before making changes, adding a safety layer not present in structured data.
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-loaded with the primary action, and every clause serves a purpose. It efficiently packs scope, parameter details, and a safety warning without redundancy.
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 tool is simple (two optional parameters), and the description covers its core behavior, scope, and user-confirmation requirement. It does not mention return values or what happens after a successful update, but given the lack of an output schema and the simplicity of the operation, this is a minor gap.
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?
Input schema has 100% coverage, but the description adds practical meaning: it clarifies that max_sends_per_day has a range and applies globally, and that auto_send_enabled=false stops all sending while true resumes it. The 'and/or' phrasing also indicates both can be updated independently. This exceeds the schema's 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's function with a specific verb ('Update') and resource ('the Gmail sending schedule'), and enumerates the two adjustable fields with their scope ('account-wide across all campaigns'). It distinguishes itself from sibling campaign-level tools by emphasizing account-wide settings.
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 context ('account-wide', 'stops ALL sending', 'resumes it') that implies when to use this tool versus per-campaign controls like pause/resume. It adds an explicit usage directive: 'Confirm with the user before changing these'. However, it does not explicitly name alternatives or state when not to use it.
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.
The tool set is largely well-disambiguated with clear prefixes (get_, outreach_, zeekeo_) and detailed descriptions for each tool. Some tools like get_site_explorer and get_site_health could be confused at a glance, but their descriptions clearly separate their purposes. Overall, the naming and descriptions prevent meaningful ambiguity.
All tools follow a consistent get_ prefix for data retrieval, with sub-prefixes like get_gsc_, get_page_, and outreach_/zeekeo_ for distinct workflows. There is no mixing of naming conventions (e.g., camelCase) or inconsistent verb usage. The naming pattern is uniform and predictable.
With 63 tools, this server vastly exceeds the typical 3-15 tool scope, categorizing as an extreme overflow. Even for a comprehensive SEO and outreach suite, the sheer number is overwhelming and likely includes redundant or overlapping functionality. This would be confusing for agents and users alike.
The tool set covers a wide range of SEO, backlink analysis, Google Search Console data, and outreach workflows, appearing nearly complete for its stated purpose. However, several tools are marked as 'v1 stub' (e.g., get_internal_links, get_schema_markup), indicating incomplete functionality. Despite these gaps, the overall coverage is extensive and well-rounded.
