Crawl Readiness MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CRAWL_READINESS_API_KEY | No | Free API key for generator tools (available at crawlreadiness.com/dashboard). Without it, the four audit tools work, but the three generator tools will not be callable. | |
| CRAWL_READINESS_API_BASE | No | Override the API base URL (for testing). Default is https://www.crawlreadiness.com. | https://www.crawlreadiness.com |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| check_ai_readinessA | Check whether AI crawlers (ChatGPT, Claude, Perplexity, Google AI, and 50+ others) can access a website. Returns a 0-100 AI readiness score, per-crawler access status, detected AI-specific files (llms.txt, agents.json), structured data presence, meta signals, and a prioritized fix list. Use this as the first step in any AI SEO audit. |
| validate_schemaA | Validate all JSON-LD structured data on a URL. Extracts every block, runs each through a rules engine covering 20+ common types (Article, Organization, Product, LocalBusiness, FAQPage, Recipe, Event, etc.), and reports required-field errors, recommended-field warnings, and type-specific gotchas. |
| validate_robotsA | Audit a robots.txt file line-by-line. Detects syntax errors, empty user-agent groups, orphan Allow/Disallow lines, non-slash paths, non-numeric Crawl-delay values, unofficial Noindex usage, and wildcard traps. Also summarizes AI-bot coverage across 50+ known AI crawlers. Provide EITHER url (to fetch and audit) OR text. |
| check_content_parityA | Compare what human browsers see vs what AI crawlers see. Fetches a page four times in parallel — as Chrome, GPTBot, ClaudeBot, and PerplexityBot — and reports word-overlap %, title/description differences, and warnings about JS-only shells, cloaking, or edge-based bot blocking. |
| generate_llms_txtA | Generate a properly formatted llms.txt file for a website. Crawls the site's sitemap, groups pages by section, pulls page titles and descriptions, and produces both the llms.txt content and a companion robots.txt snippet. The AI client can then write the returned content to disk in the user's project. Requires an API key. |
| generate_robots_txtA | Generate a complete, ready-to-save AI-crawler-aware robots.txt for a website. Fetches the existing robots.txt (if any) and returns the finished file in |
| generate_schemaA | Generate JSON-LD structured data for a website. Detects the site's name, logo, social profiles, contact info, and article metadata, then produces Organization, WebSite, and (when applicable) Article schemas plus starter templates for BreadcrumbList and FAQPage. Returns each schema as a block ready to paste into the site's . Requires an API key. |
| get_monitor_trendA | See whether AI assistants (ChatGPT, Claude, Perplexity, Google AI) actually mention a brand in their answers, and how that share-of-voice is trending versus competitors, week over week. Call with NO argument to list the user's monitored brands with each one's current mention rate and direction; pass a brand name or project id to get that brand's full trend, per-provider breakdown, competitor comparison, average position, and short example answers. Reads data the user's LLM Monitor projects have already collected — it does not trigger new runs. Read-only. Requires an API key. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 8 tools
Each tool targets a distinct phase or concern: readiness scoring, schema validation, robots auditing, content parity, file generation, and trend monitoring. The only mild ambiguity is check_ai_readiness, which overlaps at a high level with the more specialized validators, but its first-step audit role is clearly described.
All tools follow a lowercase snake_case verb_noun pattern. The mix of check_, validate_, generate_, and get_ verbs is mostly predictable, though check and validate are close synonyms that create a minor stylistic inconsistency.
Eight tools is a well-scoped size for an AI crawl readiness server. Each tool covers a meaningful capability without redundancy, and the count supports both auditing and fixing workflows.
The set covers the main audit workflow (readiness, schema, robots, content parity), generation fixes (llms.txt, robots.txt, schema), and a monitoring view. Minor gaps exist—such as no validator for generated llms.txt and no detailed meta-tag inspection—but agents can complete core tasks without dead ends.