Get a ranked list of all detectable platforms and technologies with their domain counts, useful for comparing platform adoption (e.g. how many sites run WordPress vs Shopify)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only, idempotent, and open-world, so the safety profile is clear. The description adds value by revealing the output nature (ranked list with domain counts), which is not present in annotations. No contradictions or hidden behaviors are apparent.
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 a single, well-structured sentence that front-loads the core function and includes a clarifying example. Every word contributes meaning; there is no fluff or 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?
Given the simplicity of the tool (0 parameters), the rich annotations, and the absence of an output schema, the description fully covers what the agent needs to know: it returns a global ranked list of technologies with counts. No additional context is necessary for correct invocation.
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 tool has zero parameters, and the input schema is empty. The baseline for 0 parameters is 4, and since there are no parameters to explain, the description does not need to add parameter-level details. It correctly implies that the tool takes no input.
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 uses a specific verb 'Get' and clearly identifies the resource: a ranked list of all detectable platforms and technologies with domain counts. It distinguishes itself from siblings like get_platform_domains (which likely returns domains for a specific platform) and get_tech_stack (which is probably per-domain) by emphasizing the global, aggregate nature of the data.
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 by stating it is useful for comparing platform adoption, with a concrete example (WordPress vs Shopify). It does not explicitly mention when not to use it or name alternative tools, but the use case is clear enough for an agent to infer when to select this tool over others.
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.
