SEO GEO AEO Tools — Zinin M2M Hub

AI Answer Change Alert

ai-answer-change-alert

Ask an LLM the queries that matter to you, compare each answer against its stored baseline and get told what changed — the answer itself and which domains it now cites or stopped citing. One row per query x model. — $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 check. Grounded options: `perplexity/sonar`, `openai/gpt-4o-mini:online`, `google/gemini-2.0-flash-001:online`. Every model you add multiplies the number of billed rows: rows = number of queries × number of models. With the limits on both fields, the maximum possible is 20 queries × 3 models = 60 rows.
`queries`	Yes	The prompts a real user would ask (e.g. `best crm for small business`). Each query is run against every model in `models` below and compared against the previous run's stored answer for the same query × model × language — you are charged per query × model pair, not per query, whether or not the answer changed.
`maxConcurrency`	No	Parallel LLM calls (kept low to respect rate limits and to bound how many LLM calls can be wasted — uncharged — if a run's spend limit is hit mid-run).
`openrouterApiKey`	No	Your OpenRouter API key (https://openrouter.ai/keys). Optional: without it the built-in key is used and the LLM cost is covered by the per-row price. Pass your own key to have tokens billed to your account instead, at cost.

TDQS

A4.2/5.0

Behavior4/5

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

The description discloses that answers are stored as baselines, that each call is compared and charged per query × model pair, and that pricing is $0.10 per call. This adds meaningful behavioral context beyond the annotations (openWorldHint=true, readOnlyHint=false) without contradicting them.

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

Conciseness5/5

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

The description is a single compact sentence that front-loads the main purpose, includes the key output (answer + cited domains), and states pricing and row logic. Every word earns its place with no redundancy.

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?

For a monitoring tool with five parameters, no output schema, and annotations lacking detail, the description covers the core behavior, output content, billing, and per-row scaling. It does not explain first-run baseline creation or historical retention, but the overall tool behavior is sufficiently clear.

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 documents all five parameters with rich descriptions, including billing behavior in the queries and models fields. The description adds a concise top-level pricing statement and reinforces the row calculation, but does not clarify parameter syntax or formats beyond schema coverage.

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 specifies a concrete task: ask an LLM queries, compare answers against stored baselines, and report changes (the answer plus newly/ceased cited domains). It distinguishes this from sibling tools by focusing on change detection over time rather than crawling, overview tracking, or brand 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 implies the use case: monitor LLM answers for changes to queries that matter to the user. It provides clear context and a practical billing model, though it does not explicitly name alternatives or state when not to use it.

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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