SEO GEO AEO Tools — Zinin M2M Hub

AI Overview Citation Tracker

ai-overview-tracker

Read-only

For each query that matters, see which sources and domains AI assistants cite in their answer — grounded via Perplexity Sonar, GPT or Gemini through your own OpenRouter key. GEO citation tracking: the backlink profile of the AI-answer 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
`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 project management software`, `how to choose a CRM`). 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

A3.9/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's safety profile is covered. The description adds valuable behavioral context: it uses the user's own OpenRouter key, makes external calls to specific models, and incurs a cost of $0.10/call via x402. This goes beyond the 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 about 50 words, front-loading the core purpose in the first sentence. The second sentence includes a marketing-style phrase ('backlink profile of the AI-answer era') and pricing, which are relevant but not strictly necessary. It is concise and structured effectively, though slightly embellished.

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 a rich schema covering all parameters and annotations indicating a read-only, open-world operation, the description is adequate. It communicates the main purpose, pricing, and external dependencies. It does not describe the output format, but given no output schema exists and the tool is read-only, this is a minor 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?

Schema coverage is 100%, so the description's job is to add meaning beyond the schema. The description mentions the grounding models and OpenRouter key, but the schema already details the multiplication of billing rows and the optional key behavior. The description does not significantly enhance param semantics beyond the baseline.

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 with a specific verb and resource: "see which sources and domains AI assistants cite in their answer." It further differentiates itself from siblings by introducing the concept of "GEO citation tracking: the backlink profile of the AI-answer era," which is distinct from change alerts, crawler checks, or brand visibility tools.

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

Usage Guidelines3/5

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

The phrase "For each query that matters" implies a use case for SEO/GEO professionals but does not explicitly state when to use this instead of alternatives or provide exclusions. No sibling tools are mentioned, so the guidance is only implied.

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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