SEO GEO AEO Tools — Zinin M2M Hub

LLM Brand Visibility Tracker

llm-brand-visibility

Read-only

For each query that matters, check whether AI assistants recommend YOUR brand — and which competitors they cite instead. Grounded answers from Perplexity Sonar, GPT and Gemini via your own OpenRouter key. This is GEO: the SEO of the AI era. — $0.10/call, x402 (USDC on base).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lang`	No	Answer language (e.g. en, ru, tr).	en
`brand`	Yes	The brand, product or company you want to track in AI answers.
`models`	No	OpenRouter model IDs to test. Grounded options: `perplexity/sonar`, `openai/gpt-4o-mini:online`, `google/gemini-2.0-flash-001:online`. Every model you add here multiplies the number of billed rows: rows = number of queries × number of models (see `queries`).
`queries`	Yes	The prompts a real user would ask (e.g. `best note-taking app`, `Notion alternatives`). Each query is run against EVERY model in `models` below, and you are charged per query × model pair, not per query — e.g. 10 queries × 2 models = 20 billed rows. With the limits on both fields, the maximum possible is 20 queries × 5 models = 100 rows.
`maxConcurrency`	No	Parallel LLM calls (kept low to respect rate limits).
`openrouterApiKey`	No	Your OpenRouter API key (https://openrouter.ai/keys). LLM token cost is billed to YOUR account; this Actor only orchestrates and scores. Optional to start the Actor: without it the run finishes cleanly and explains setup, and nothing is charged beyond the Actor start.

TDQS

B3.3/5.0

Behavior4/5

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

Beyond the readOnlyHint and destructiveHint annotations, the description discloses that the tool uses the user's own OpenRouter key, implying authentication requirements, and mentions a $0.10/call cost with x402 payment. This adds useful operational context, though it doesn't mention rate limits or side effects.

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?

The description is four sentences and front-loads the primary purpose, then adds technical and pricing details. The GEO marketing phrase is arguably extraneous, but the overall length is appropriate; it earns a 4.

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

Completeness2/5

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

The tool has no output schema, and the description does not explain the result format, how 'visibility' is measured, or what the response looks like to the agent. The cryptic '$0.10/call, x402' also lacks context. With six parameters and no output schema, this is a significant gap.

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

Parameters3/5

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

The input schema already describes all parameters with 100% coverage, including billing multipliers and API key optionality, so the description adds little parameter-level meaning. Baseline 3 is appropriate since the schema does the heavy lifting.

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 clearly states the tool checks whether AI assistants recommend a given brand and cites competitors, which distinguishes it from sibling tools like ai-crawler-access-checker or social-preview-checker. However, the verb 'check' is somewhat generic and the output format isn't specified, so it doesn't fully earn a 5.

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

Usage Guidelines2/5

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

No explicit when-to-use or alternatives are mentioned. The phrase 'For each query that matters' implies suggested usage, and the GEO analogy provides context, but there's no guidance on when not to use this tool or how it compares to ai-answer-change-alert or ai-overview-tracker. This earns a 2.

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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TDQS

A3.9/5.0

Disambiguation5/5

Each tool targets a distinct aspect of SEO/GEO/AEO: AI answer changes, crawler access, cited sources, brand visibility, llms.txt auditing, pricing, and social previews. No two tools overlap in purpose, ensuring clear selection.

Naming Consistency4/5

Most tools use descriptive snake_case with hyphens (e.g., ai-answer-change-alert), but pricing_info breaks the pattern with an underscore. Overall, names are clear and follow a logical prefix system (ai-, llms-, social-).

Tool Count5/5

Seven tools is an ideal size for a specialized MCP server. Each tool has a clear function, and the count is neither sparse nor overwhelming.

Completeness4/5

The set covers core GEO/AEO workflows (crawler access, LLM answers, brand visibility, llms.txt) plus social previews and pricing. Minor gaps exist (e.g., no keyword or competitor analysis), but the domain is well-served.

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