One-call audit: robots.txt AI-bot rules, llms.txt, JSON-LD, meta tags, sitemap. Score plus fixes.
Scored across 1 tool
With only a single tool, there is zero risk of confusing it with another. The tool's purpose is clearly defined and unambiguous, covering a specific audit task.
The tool name 'audit_website' follows a clear verb_noun pattern. Since it is the only tool, the naming convention is perfectly consistent and predictable.
The server has just one tool, which is below the typical 3-15 range for good scoping. While the server's purpose is narrow, a single tool feels thin and could potentially be split into separate checks (e.g., robots, llms.txt).
The tool comprehensively covers the AI visibility audit domain: robots.txt bot rules, llms.txt, JSON-LD, meta tags, and sitemap, plus actionable findings and fixes. There are no obvious gaps for an audit-only server.
Audit whether AI crawlers may read a site and whether AI search can cite it. Checks robots.txt bot rules (GPTBot, OAI-SearchBot, ClaudeBot, PerplexityBot, Google-Extended, CCBot …), llms.txt, JSON-LD, meta tags and sitemap. Returns {score, grade, blocked_bots, findings[], fixes[]}.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | site URL or bare domain, e.g. example.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses what it checks (robots.txt, llms.txt, etc.) and the return structure, implying a read-only audit, but it does not explicitly state safety, potential network latency, or any side effects. This is adequate but leaves some behavioral details to inference.
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?
The description is two concise sentences, front-loaded with the primary purpose, followed by a list of checks and the return format. Every clause adds value with no redundancy or fluff.
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?
The description includes the specific checks performed and the return structure ({score, grade, blocked_bots, findings[], fixes[]}), which is sufficient for an agent to understand the tool's function and output. It does not cover error handling or edge cases, but for a single-parameter audit tool with a well-defined return, it is nearly complete.
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?
The schema provides 100% coverage for the single 'url' parameter with a description of 'site URL or bare domain, e.g. example.com'. The tool description itself adds no additional semantics about the parameter, so the baseline of 3 is appropriate since the schema already handles documentation.
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?
The description clearly states the tool's purpose: auditing whether AI crawlers may read a site and whether AI search can cite it. It lists specific checks (robots.txt, llms.txt, JSON-LD, meta tags, sitemap) and identifies a distinct resource, making it unambiguous even without siblings.
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?
The description provides clear context about what the tool does and what it returns, which implicitly indicates when to use it. There are no sibling tools to differentiate against, and no explicit exclusions are mentioned, but the purpose is self-evident enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
audit_websiteAudits AI-crawler access, structured data and llms.txt on a site, and drafts the fixes.
Scan and fix your site's AI discoverability: crawler access, llms.txt, JSON-LD. Free.
Audit any website for AI visibility: graded report, findings with fixes, AI crawler access check.
Audit a page for search and AI answer engines; generate robots.txt, sitemap, head, llms.txt.
Add one secure layer between your agents and this server.