Score how ready a website is for AI agents and answer engines. Fetches /llms.txt, /robots.txt, and the homepage and returns a letter grade with per-signal findings. No browser rendering or LLM call — deterministic HTTP/parse.
| Name | Required | Description | Default |
|---|---|---|---|
| hostname | Yes | A public hostname, e.g. example.com |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotation only declares readOnlyHint: true, but the description adds valuable behavioral context: it fetches specific files, returns a letter grade with findings, and explicitly states 'No browser rendering or LLM call — deterministic HTTP/parse.' This goes beyond the annotation and helps the agent understand exactly what the tool does and its limitations.
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 sentences, front-loads the core purpose, and packs essential details (exact resources fetched, output type, deterministic method) with no redundant words. Every sentence earns its place, making it highly concise and well-structured.
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 parameter, a read-only annotation, and no output schema, the description is complete. It covers what the tool does, how it works (HTTP/parse), what it returns, and its non-browser/non-LLM nature. There is no missing information that would hinder an agent from invoking it 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 fully documents the single parameter 'hostname' with description 'A public hostname, e.g. example.com' (100% coverage). The description adds no additional meaning about parameter formatting or constraints beyond what the schema already provides, so the baseline 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 clearly states the tool's purpose: 'Score how ready a website is for AI agents and answer engines.' It specifies the exact resources fetched (/llms.txt, /robots.txt, homepage) and the output (letter grade with per-signal findings). This distinguishes it from sibling tools like check_domain_health and check_mcp_health, which focus on different aspects.
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 for when to use this tool—when assessing a website's AI readiness. It does not explicitly mention alternatives or exclusions, but the purpose is self-evident given the sibling tool names. The exclusion of browser rendering and LLM calls gives a hint about its appropriate use cases, but no direct comparison to alternatives is provided.
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 resource and action. The five check tools are differentiated by their target (agent readiness, broken links, domain health, email blacklist, MCP health), and asset/alert management tools have clear CRUD/lifecycle boundaries. No two tools appear to overlap.
All 16 tools follow a consistent verb_noun pattern with lowercase and underscores (e.g., check_domain_health, create_asset, list_my_alerts). The verbs are clear and consistently used, making the API predictable.
16 tools is well-scoped for a monitoring service that spans asset management, alert lifecycle, multiple diagnostic checks, vendor status, and plan listing. Every tool has a distinct role and earns its place without redundancy.
The tool surface covers full asset CRUD (create, list, update, delete, get checks), alert lifecycle (list, acknowledge, resolve), and a broad set of standalone checks. Minor gaps like a direct 'get asset' endpoint or manual re-check are absent, but core workflows are fully supported.
