Website Auditor MCP

Benchmark vs industry/geo

get_benchmark

Read-only

Benchmark a website's AI visibility against industry and location peers, providing percentile context to evaluate performance relative to similar sites.

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

A4.7/5.0

Behavior5/5

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

Annotations already mark the tool as read-only and non-destructive, and the description adds a crucial behavioral constraint: the tool requires a Website Auditor subscription, including free-trial details and the fact that payment information is needed upfront. It also discloses that results are backed by aggregated audit data.

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?

Every sentence contributes a distinct piece of information: what the tool does, when to use it, how the data is derived, the subscription requirement, and the fallback path. The purpose is front-loaded, and no unnecessary boilerplate is present.

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?

An output schema exists, so return-value details are not the description's job. The description covers the prerequisite (subscription), the fallback flow (get_sample_audit), the use-case signals, and the underlying data source. Nothing critical is missing for an agent to decide when to call this 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?

All three parameters already have schema descriptions, so the baseline is 3. The tool description does not add much parameter-level detail beyond implying that geo and industry are optional overrides used for benchmarking context, which the schema already states.

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 opens with a specific verb and object: 'Benchmark a website's AI visibility against its industry and location,' and clarifies that the user wants percentile/peer context rather than an absolute number. This distinguishes it from tools like get_ai_visibility and compare_competitors by making the benchmark scope (industry/geo) explicit.

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 description gives explicit trigger phrases ('how do I compare to others in my space,' 'is this a good score for my industry') and tells the agent to call get_sample_audit first when the user lacks a subscription. This is clear, actionable routing guidance with a named fallback.

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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