Website Auditor MCP

Benchmark vs industry/geo

get_benchmark

Read-only

Compare a website's AI visibility score against industry and location benchmarks to see percentile rank and peer context.

Instructions

Benchmark a website's AI visibility against its industry and location. Use this when someone asks "how do I compare to others in my space," "is this a good score for my industry," or wants percentile/peer context rather than an absolute number. Backed by aggregated audit data. 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

Name	Required	Description
`geo`	No	Optional location override.
`domain`	Yes	The website domain, e.g. "example.com".
`industry`	No	Optional industry override.

Output Schema

TableJSON Schema

Name	Required	Description
`percentile`	Yes	The domain's percentile within its industry/geo peer set, 0–100.
`peer_median`	Yes
`sample_size`	Yes	How many peers the percentile and median are computed over.
`position_summary`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.0.23Sep 9, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "additionalProperties": false,
+  "properties": {
+    "peer_median": {
+      "type": "number"
+    },
+    "percentile": {
+      "description": "The domain's percentile within its industry/geo peer set, 0–100.",
+      "type": "number"
+    },
+    "position_summary": {
+      "type": "string"
+    },
+    "sample_size": {
+      "description": "How many peers the percentile and median are computed over.",
+      "type": "number"
+    }
+  },
+  "required": [
+    "percentile",
+    "peer_median",
+    "sample_size",
+    "position_summary"
+  ],
+  "type": "object"
+}

First observedv1.0.6Jul 29, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false. The description adds valuable non-annotation context: subscription/payment requirements, trial terms, and the fallback to get_sample_audit. Does not disclose latency or data-freshness, but the billing/auth context is the key behavioral note here.

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

Conciseness4/5

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

Front-loads the core verb and resource, then provides usage triggers. The subscription paragraph is long but earns its place as operational necessity. Slightly dense but well-ordered.

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?

Output schema exists so return format needn't be explained. The description covers purpose, triggers, cost/auth prerequisites, and fallback path – everything needed to invoke 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 baseline is 3. The description adds meaning beyond the schema by clarifying that geo and industry act as overrides (implying defaults are derived from the domain), which the schema does not state.

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?

States a specific verb+resource ('Benchmark a website's AI visibility') with explicit scope (against industry and location), distinguishing it from absolute-score siblings like get_ai_visibility.

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?

Gives explicit when-to-use triggers with quoted user phrasings ('how do I compare to others,' 'is this a good score for my industry') and names the alternative get_sample_audit for users lacking a subscription. Clear routing logic.

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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