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SpikeyCoder

Website Auditor MCP

by SpikeyCoder
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Run a full audit

run_audit
Read-only

Run a complete website audit covering AI visibility, SEO, security headers, broken links, and performance. Get a scored summary across categories plus a link to the full report.

Instructions

Run a full one-time audit of a website — AI visibility plus SEO, security headers, broken links, and performance. Use this when someone asks to "audit," "scan," "check," or "review" a website's health or SEO, or wants a complete report rather than just the AI-visibility number. Returns a scored summary across categories and a link to the full report. The ranked cited-sources evidence behind the AI-visibility number is returned by get_ai_visibility, not by this tool. If name_warning is present, the business name the AI-visibility score was measured against could not be verified — relay that caveat rather than presenting the score as settled fact. Requires a Website Auditor subscription ($10/month; eligible new customers get a 7-day free trial — payment method required, no charge until the trial ends) — if the user doesn't have one, call get_sample_audit first to show them the exact output format, free and with no API key.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesThe website domain, e.g. "example.com".
business_nameNoOptional. The business's real name, if you know it. Leave it out and the audit detects the name from the site and flags it when unverified — a guessed name is scored as if confirmed, so supply one only when it is actually known.
business_locationNoOptional. The city the business trades in, e.g. "Hilo, HI". Leave it out and the audit detects it; when nothing is detectable the questions widen to the country or drop the place entirely, which is right for a national or global business and wrong for a local one.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed2 schema fields changedv1.0.15
    • addedInput schema / properties / business_location
      Added value: +{
+  "description": "Optional. The city the business trades in, e.g. \"Hilo, HI\". Leave it out and the audit detects it; when nothing is detectable the questions widen to the country or drop the place entirely, which is right for a national or global business and wrong for a local one.",
+  "type": "string"
+}
    • addedInput schema / properties / business_name
      Added value: +{
+  "description": "Optional. The business's real name, if you know it. Leave it out and the audit detects the name from the site and flags it when unverified — a guessed name is scored as if confirmed, so supply one only when it is actually known.",
+  "type": "string"
+}
  2. First observedv1.0.6

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the readOnlyHint and destructiveHint annotations, the description discloses several important behaviors: the audit is one-time, returns a scored summary and report link, requires a paid subscription with trial terms, may return a name_warning that must be relayed as a caveat, and warns that an unverified business name is scored as if confirmed. This is rich behavioral context an agent needs to handle the result responsibly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence carries operational value: scope, trigger conditions, output summary, sibling-tool distinction, caveat handling, and subscription gate. It is front-loaded with the core purpose and then layers necessary detail without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having no output schema, the description explains what the tool returns (scored summary, report link, possible name_warning) and covers subscription requirements, fallback behavior, and free-alternative routing. For a tool with optional parameters and real-world caveats, this is complete enough for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds critical semantic warnings: business_name should only be supplied when actually known because a guessed name is scored as confirmed, and business_location omission triggers detection with fallback behavior. This meaningfully improves correct parameter use beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Run a full one-time audit of a website') and enumerates what the audit covers: AI visibility, SEO, security headers, broken links, and performance. It also explicitly distinguishes itself from get_ai_visibility, eliminating ambiguity about which tool returns the cited sources evidence.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit trigger phrases ('audit,' 'scan,' 'check,' 'review'), clarifies the complete-report use case, and tells the agent exactly when to use get_sample_audit instead (no subscription). This is model guidance for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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