check_lighthouse_agentic_browsingPrüft einen llms.txt-Inhalt exakt gegen die drei Regeln des Lighthouse-Audits llms-txt (Lighthouse 13.3): H1 vorhanden, mindestens ein Markdown-Link, mehr als 50 Zeichen. Benennt bei Fehlschlag die Originalmeldung von Lighthouse.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Der Inhalt der llms.txt. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHnt annotation already signals a safe read operation, and the description adds real behavioral detail: exactly which three rules are checked and that failures report the original Lighthouse message. This goes beyond the annotations and does not contradict them.
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 a single dense sentence that front-loads the core action, then lists the exact rules and failure behavior. There is no filler or redundancy; every clause contributes necessary information.
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?
For a simple tool with one fully documented string parameter, the description is mostly complete: it states the audit rules and what happens on failure. The only gap is the unspecified success return format, but since no output schema exists and the failure behavior is described, an agent can still select and invoke the tool correctly.
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 input schema already describes the single 'content' parameter fully, including its maxLength, and schema description coverage is 100%. The tool description adds no new parameter-level detail, so the baseline score of 3 is appropriate.
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 names a specific verb ('prüft'), the exact resource ('llms.txt-Inhalt'), and the precise audit rules (H1, one Markdown link, >50 characters) from a named audit version (Lighthouse 13.3). This clearly distinguishes it from sibling tools like validate_llms_txt or check_published_llms_txt.
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?
It does not explicitly name alternative tools or exclusions, but the context is clear: use this when you need to check an llms.txt content against the Lighthouse llms-txt audit rules. The usage context is implied strongly enough for an agent to select it correctly, though explicit sibling routing would be better.
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 has a distinct role: generate creates a draft, validate scores arbitrary content against llmstxt.org, check_lighthouse applies exact Lighthouse rules, and check_published fetches a live URL. The only mild overlap is that validate also reports Lighthouse status, but the descriptions clarify the different focus.
Names are all snake_case verb-first phrases such as generate_llms_txt and check_published_llms_txt. check_lighthouse_agentic_browsing breaks the llms_txt-noun pattern slightly and is awkward, but the verb-first convention is otherwise consistent.
With four tools, the server is tightly scoped to the llms.txt workflow: creation, validation, Lighthouse auditing, and live publishing checks. Each tool addresses a clear need without bloat.
The set covers the full practical lifecycle: generate a draft, validate against the standard, check Lighthouse compliance, and verify the published file. No obvious dead ends or missing core operations.