Website Auditor MCP

Benchmark vs industry/geo

get_benchmark

Read-only

Benchmark a website's AI visibility against industry and location peers to see percentile rankings and determine whether a score is good for its space.

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 ($49/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

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

Output 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

A4.5/5.0

Behavior5/5

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

The description adds essential behavioral context beyond annotations, specifically detailing the subscription model, the cost structure, and the necessary preliminary call (get_sample_audit) for free users. This manages user expectations and guides agent pre-checks effectively.

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

Conciseness3/5

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

While highly informative, the description is lengthy due to its comprehensive nature regarding business logic. However, every sentence serves a distinct purpose (definition, use case, access requirements), preventing excessive bloat.

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?

Given the required payment barrier and the need to guide the agent through the user's subscription status, this definition is maximally complete. It leaves no ambiguity regarding when this tool is usable versus the related sample tool.

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

Parameters3/5

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

Since the schema description coverage is 100%, the description provides no new semantic information about the `domain`, `geo`, or `industry` parameters beyond what the structured schema already provides. It aligns with the baseline score for high coverage.

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 clearly defines the purpose: benchmarking a website's AI visibility against its industry and location. It distinguishes this specialized, relative comparison from general audit retrieval by framing the goal around peer context.

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?

The definition provides explicit use cases by listing exact user queries (e.g., 'is this a good score for my industry'). Crucially, it outlines the prerequisite logic: use this only if the user has a subscription, otherwise use get_sample_audit.

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