check_ai_visibilityAEO check: whether AI answer engines (ChatGPT/GPTBot, Claude, Perplexity, Google-Extended, etc.) can crawl and cite the site, plus llms.txt and structured-data signals.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Full https URL to check |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint and openWorldHint. Description adds specific crawler list and signal types, providing useful behavioral context without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with 'AEO check', no wasted words. Efficient and direct.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one param and no output schema. Description covers what is checked (engines, signals) adequately. Slightly lacking on output format but acceptable given simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with url parameter fully described. Description adds no additional parameter meaning beyond schema, meeting baseline expectation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool checks AI answer engine visibility, specifying exact engines (ChatGPT, Claude, etc.) and signals (llms.txt, structured-data). Distinct from sibling tools that focus on security, email, SEO, etc.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Context is clear via 'AEO check' and specific engines listed, but no explicit when-not-to-use or alternatives mentioned. Implied usage is appropriate for AI crawler audits.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct aspect of website analysis: AI visibility, email security, exposed files, security headers, SEO, SSL, and a general scan. No two tools overlap in purpose, making it easy for an agent to select the right one.
Six of seven tools follow the 'check_<subject>' pattern, which is clear and consistent. The outlier 'scan_website' breaks the pattern but is still understandable. Overall, naming is predictable with a minor deviation.
Seven tools is an appropriate number for a website scanning/analysis server. It covers key areas (security, SEO, AI visibility) without being overwhelming. Each tool earns its place by addressing a specific need.
The tool surface covers major website concerns: security (headers, SSL, exposed files, email), SEO, and AI visibility. The general 'scan_website' tool likely fills gaps like performance and secrets. Missing some niche checks (e.g., accessibility), but the core is well-covered.