AI Readiness (hosted)
Server Details
Check if a website is visible to AI search (ChatGPT, Perplexity, Claude). No install.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool access control
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Managed credentials
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
Both tools have distinct purposes: one assesses AI readiness, the other generates a fix pack. No overlap in functionality.
Both names follow a consistent verb_noun pattern: check_ai_readiness and generate_complete_fix_pack.
With only 2 tools, the server is minimal but well-scoped for its check-and-fix workflow. Slightly thin but not problematic.
The tools cover assessment and remediation, completing the main workflow. Minor gaps (e.g., no separate update tool) are acceptable given the paid fix pack handles everything.
Available Tools
2 toolscheck_ai_readinessAInspect
Check whether a website is visible to AI search engines (ChatGPT, Perplexity, Claude, Google AI Overviews). Scores AI-crawler access, JSON-LD structured data, title/meta, Open Graph, sitemap, and llms.txt, and returns a 0-100 score, a letter grade, and a specific fix for each gap. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website to check, e.g. example.com or https://example.com |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. It discloses checks and return values (score, grade, specific fix). It does not mention limitations or side effects, but as a read-only check, this is adequate.
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 concise (three sentences), front-loaded with purpose, and wastes no words. It efficiently communicates key 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 parameter and no output schema, the description covers purpose, inputs, and outputs sufficiently. Lacks mention of error handling or edge cases, but not critical for this use case.
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 coverage is 100% and the parameter description is clear. The tool description adds no further parameter information beyond what the schema provides. Baseline 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 checks AI search engine visibility, lists specific components checked, and returns a score and grade. It is distinct from the sibling tool 'generate_complete_fix_pack', which likely applies fixes.
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 implies usage for assessing AI readiness and mentions it's free, but does not explicitly state when to avoid using it or compare with the sibling tool. Still, context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_complete_fix_packAInspect
PAID. Returns the complete, ready-to-paste AI-readiness Fix Pack for a site: tailored Organization + FAQPage JSON-LD, an AI-crawler robots.txt, a sitemap, and title/meta/Open Graph fixes. Requires a license — the checkout-session id you receive after buying the $39 Fix Pack at https://buy.stripe.com/8x24gA0xA9DF9dd13YeZ20h (after paying you're shown your license code). Without a valid license it returns purchase instructions plus a free starter pack.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website to generate the Fix Pack for. | |
| license | No | Your Stripe checkout-session license code (starts with cs_). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Since no annotations are provided, the description fully discloses that the tool is paid, requires a license, and returns different content based on license validity. It does not mention any side effects, but as a generation tool, none are expected.
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-loaded with 'PAID' to indicate cost, efficiently covering purpose, requirements, purchase link, and fallback. No wasted words.
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 the tool's simplicity (2 parameters, fully described in schema, no output schema), the description adequately explains output components and license requirement. It could mention the format of the returned data but is sufficiently complete for usage.
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%, providing baseline of 3. The description adds significant context: it explains the license parameter's origin (Stripe session ID), the purchase URL, and the fallback behavior, which enhances understanding beyond the schema.
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 generates a complete AI-readiness Fix Pack including specific components, and distinguishes it from the sibling tool 'check_ai_readiness' which checks readiness rather than generates fixes.
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 explains that the tool is paid and requires a license from Stripe, and provides a URL for purchase. It also mentions the fallback behavior (purchase instructions + free starter pack) if no valid license. However, it does not explicitly compare usage with the sibling tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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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
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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
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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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