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Ubersuggest MCP Server and API by Cotlet

Get site audit in Ubersuggest

ubersuggest_get_site_audit
Read-onlyIdempotent

Get site audit in the user's own Ubersuggest account from ChatGPT, Claude or any AI agent. Returns how to do it in Ubersuggest step by step (screens, buttons, fields) and opens nothing by itself; then do it with the cotlet_browser_* tools in the user's Ubersuggest Cloud Browser. Ubersuggest's official API: Ubersuggest has no public REST API and no webhooks (checked 23 Oct 2026).

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ubersuggest_get_site_audit"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so safety is covered. The description still adds real value beyond them: it states the return is procedural guidance (screens, buttons, fields), that the call performs no UI action itself, and that a browser-tool handoff is required. The closing note about no public REST API and no webhooks is useful context but slightly tangential.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the purpose and the key surprise (supervised guidance, no side effects), followed by the workflow handoff. The API/webhooks sentence is the weakest part and borders on boilerplate, but it can save the agent from searching for an API route.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no parameters, the description carries the burden of explaining what comes back, and it does ('how to do it in Ubersuggest step by step (screens, buttons, fields)'). Combined with the explicit follow-up tool family and the no-API note, an agent has enough to call it correctly; only sibling differentiation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are zero parameters and schema coverage is 100%, so the baseline is 4. The description correctly adds nothing about parameters, and none is needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific resource (site audit) and goes further than the title by revealing the non-obvious deliverable: it returns step-by-step how-to guidance rather than audit data, and 'opens nothing by itself'. That is a meaningful clarification. It does not, however, distinguish this from the sibling ubersuggest_recrawl_site_audit, leaving the agent to infer which audit-related tool to pick.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a concrete execution path ('then do it with the cotlet_browser_* tools in the user's Ubersuggest Cloud Browser'), which tells the agent exactly what to do after calling. It stops short of explicit exclusions or when-not conditions, so there is no explicit routing against siblings such as ubersuggest_recrawl_site_audit.

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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