Check whether AI agents can read any website: 18 deterministic checks, each with its fix.
Check whether AI agents can read a website. Runs 19 deterministic checks across llms.txt, bot policy, structured data and machine endpoints, and returns every finding with an explanation and a fix. Free, read-only, and stores nothing.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The site to check — a hostname or a full URL, e.g. example.com |
| Name | Required | Description |
|---|---|---|
| score | Yes | 0-100 over scored, applicable checks. Null when nothing scorable could be determined. |
| summary | Yes | |
| findings | Yes | |
| hostname | Yes | |
| checkedAt | Yes | ISO 8601 timestamp of the crawl. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description goes beyond that by adding 'deterministic checks,' listing the check categories, stating the output format ('every finding with an explanation and a fix'), and adding 'stores nothing' — a new behavioral detail not covered by the annotations. It consistently reinforces the read-only nature without contradicting the structured hints.
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 three sentences with zero waste. The core purpose is front-loaded, followed by scope, output, and safety guarantees. Every sentence contributes necessary information, and there is no redundant 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?
Given that an output schema exists and the description covers purpose, scope, determinism, output format, and read-only/non-storing behavior, nothing essential is missing for an agent to correctly select and invoke the tool. The description is sufficiently complete for a read-only check of website AI-readiness.
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 description coverage is 100% for the single 'url' parameter, which already specifies a hostname or full URL. The tool description adds no further semantic detail about the parameter. Since the schema fully documents the parameter, the description's lack of extra parameter information is acceptable, but it doesn't elevate beyond the baseline.
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 opens with a specific verb and resource: 'Check whether AI agents can read a website.' It then quantifies the scope ('19 deterministic checks') and lists the checked areas (llms.txt, bot policy, structured data, machine endpoints). This is unambiguous and immediately distinguishes the tool from any hypothetical alternative.
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 purpose is explicit and self-contained, so an agent knows exactly when this tool applies. There are no sibling tools to differentiate against, so explicit exclusions are unnecessary. The description could add a 'not for' hint but the clarity of the use case makes that optional.
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. Dates show when Glama detected each change.
scan_siteClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Checks whether a website is readable and citable by AI systems (ChatGPT, Claude, Perplexity, etc.)
Scores any public website on how usable it is by AI agents, with per-check evidence.
Check how well AI agents can discover and understand a public website.
Check website AI-readiness: Schema.org, llms.txt, E-E-A-T, robots.txt. Works in Cursor & Claude.
Add one secure layer between your agents and this server.
Only one tool exists, so there is no possibility of confusion between tools. The tool's description clearly defines its single purpose of scanning websites for AI-agent readability.
The single tool name 'scan_site' follows the conventional verb_noun snake_case pattern. With only one tool, there are no inconsistencies to evaluate.
A single tool feels thin for a server named 'Sichta Web', which might imply a broader web-focused toolkit. However, the tool itself is comprehensive (19 checks), so it is borderline rather than trivial.
The tool covers all major areas for AI-agent readability: llms.txt, bot policy, structured data, and machine endpoints. It provides explanations and fixes, making it a complete solution for its stated purpose.
