SEO Tools

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

A4/5.0

Behavior4/5

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

Annotations already declare readOnly=true, openWorld=true, destructiveHint=false, so the safety profile is covered. The description adds behavioral context beyond these: it uses your own OpenRouter key, calls external models (Perplexity Sonar, GPT, Gemini), and incurs a per-call cost. No contradiction with annotations.

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 concise: two sentences, with the main purpose front-loaded in the first sentence. The second sentence adds technical context and pricing. The 'GEO: the SEO of the AI era' tagline is slightly promotional but not enough to penalize heavily.

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

Completeness3/5

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

With no output schema, the description should explain what the user gets back, but it does not describe the result format or scoring metrics. It does cover external dependencies, cost, and model grounding, but the missing output behavior leaves a gap for full contextual completeness.

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?

Schema coverage is 100%, and each parameter already has a rich description (e.g., queries × models billing, max limits, OpenRouter key requirements). The main description adds no extra parameter-level details, so a baseline 3 is appropriate.

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

Purpose5/5

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

The description clearly states the tool's function: 'check whether AI assistants recommend YOUR brand — and which competitors they cite instead.' This employs a specific verb (check/recommend) and resource (AI assistant answers), and it distinguishes this tool from siblings like ai-overview-tracker by focusing on brand vs competitor visibility.

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?

The description conveys clear usage context ('For each query that matters') and value proposition, implying this is for monitoring brand presence in AI responses. However, it does not explicitly mention when to use this tool over alternatives or provide exclusions, so it stops short of a 5.

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

A4/5.0

Disambiguation2/5

Several tools have overlapping purposes: ai-crawler-access-checker and llms-txt-auditor both check robots.txt for AI crawler access, while ai-answer-change-alert, ai-overview-tracker, and llm-brand-visibility all track AI-generated citations/visibility. An agent may struggle to pick the right one without reading full descriptions.

Naming Consistency4/5

Most tools use lowercase hyphenated noun phrases (e.g., ai-answer-change-alert, social-preview-checker), but pricing_info uses an underscore, breaking the otherwise consistent pattern. Minor deviation but still readable.

Tool Count5/5

Seven tools is well-scoped for a specialized SEO/AI-visibility server. Each tool addresses a distinct aspect (crawler access, llms.txt, AI citations, brand visibility, social previews) without being excessive, and the pricing_info helper is a sensible addition.

Completeness4/5

The set covers the core AI-era SEO workflow: audit AI crawler access, validate llms.txt, track AI overview citations, monitor answer changes, and check brand visibility. Missing obvious features like generating llms.txt or deeper competitor analysis, but no critical dead ends.

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